This file was created by the Typo3 extension sevenpack version 0.7.14 --- Timezone: CEST Creation date: 2017-05-23 Creation time: 10-49-56 --- Number of references 69 article EckerDBT2016 On the Structure of Neuronal Population Activity under Fluctuations in Attentional State Journal of Neuroscience 2016 2 36 5 1775-1789 Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations, even if unknown to a downstream readout, do not impair the readout accuracy despite inducing limited-range correlations, whereas fluctuations of the attended feature can in principle limit behavioral performance. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.jneurosci.org/content/36/5/1775.full.pdf+html 10.1523/JNEUROSCI.2044-15.2016 aeckerASEcker GHDenfield mbethgeMBethge atoliasASTolias article JiangSCBSEPT2015 Principles of connectivity among morphologically defined cell types in adult neocortex Science 2015 11 350 6264 1055: 1-10 Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.sciencemag.org/content/350/6264/aac9462.full.pdf 10.1126/science.aac9462 aac9462 XJiang SShen CRCadwell berensPBerens fabeeFSinz aeckerASEcker SPatel atoliasASTolias article GatysEB2015_3 A Neural Algorithm of Artistic Style Nature Communications 2015 10 In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://arxiv.org/abs/1508.06576 submitted LAGatys aeckerASEcker mbethgeMBethge article GatysETB2015_2 Synaptic unreliability facilitates information transmission in balanced cortical populations Physical Review E 2015 6 91 062707 1-7 Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level. PDFHTML http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://journals.aps.org/pre/pdf/10.1103/PhysRevE.91.062707 10.1103/PhysRevE.91.062707 LAGatys aeckerASEcker TTchumatchenko mbethgeMBethge article YatsenkoJEFCT2015 Improved Estimation and Interpretation of Correlations in Neural Circuits PLoS Computational Biology 2015 3 11 3 1-28 Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.ploscompbiol.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pcbi.1004083&representation=PDF 10.1371/journal.pcbi.1004083 e1004083 DYatsenko KJosić aeckerASEcker EFroudarakis RJCotton atoliasASTolias article EckerT2014 Is there signal in the noise? Nature Neuroscience 2014 6 17 6 750-751 A study now shows that variability in neuronal responses in the visual system mainly arises from slow fluctuations in excitability, presumably caused by factors of nonsensory origin, such as arousal, attention or anesthesia. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.nature.com/neuro/journal/v17/n6/pdf/nn.3722.pdf 10.1038/nn.3722 aeckerASEcker atoliasASTolias article FroudarakisBECSYSBT2014 Population code in mouse V1 facilitates readout of natural scenes through increased sparseness Nature Neuroscience 2014 6 17 6 851–857 Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge Department Schölkopf http://www.nature.com/neuro/journal/v17/n6/pdf/nn.3707.pdf 10.1038/nn.3707 EFroudarakis berensPBerens aeckerASEcker RJCotton fabeeFHSinz DYatsenko PSaggau mbethgeMBethge atoliasASTolias article EckerBCSDCSBT2014 State Dependence of Noise Correlations in Macaque Primary Visual Cortex Neuron 2014 4 82 1 235–248 Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1–2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations but can also be estimated and accounted for based on neuronal population activity. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Department Schölkopf http://www.sciencedirect.com/science/article/pii/S0896627314001044 10.1016/j.neuron.2014.02.006 aeckerASEcker berensPBerens RJCotton MSubramaniyan GHDenfield CRCadwell ssmirnakisSMSmirnakis mbethgeMBethge atoliasASTolias article SubramaniyanEBT2013 Macaque Monkeys Perceive the Flash Lag Illusion PLoS ONE 2013 3 8 3 1-10 Transmission of neural signals in the brain takes time due to the slow biological mechanisms that mediate it. During such delays, the position of moving objects can change substantially. The brain could use statistical regularities in the natural world to compensate neural delays and represent moving stimuli closer to real time. This possibility has been explored in the context of the flash lag illusion, where a briefly flashed stimulus in alignment with a moving one appears to lag behind the moving stimulus. Despite numerous psychophysical studies, the neural mechanisms underlying the flash lag illusion remain poorly understood, partly because it has never been studied electrophysiologically in behaving animals. Macaques are a prime model for such studies, but it is unknown if they perceive the illusion. By training monkeys to report their percepts unbiased by reward, we show that they indeed perceive the illusion qualitatively similar to humans. Importantly, the magnitude of the illusion is smaller in monkeys than in humans, but it increases linearly with the speed of the moving stimulus in both species. These results provide further evidence for the similarity of sensory information processing in macaques and humans and pave the way for detailed neurophysiological investigations of the flash lag illusion in behaving macaques. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis http://www.plosone.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0058788&representation=PDF 10.1371/journal.pone.0058788 e58788 MSubramaniyan aeckerASEcker berensPBerens atoliasASTolias article BerensECMBT2012 A Fast and Simple Population Code for Orientation in Primate V1 Journal of Neuroscience 2012 8 32 31 10618-10626 Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30–80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that—similar to the case of single-neuron computation—the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Department Schölkopf http://www.jneurosci.org/content/32/31/10618.full.pdf+html 10.1523/​JNEUROSCI.1335-12.2012 PBerens aeckerASEcker RJCotton WJMa mbethgeMBethge atoliasASTolias article EckerBTB2011 The effect of noise correlations in populations of diversely tuned neurons Journal of Neuroscience 2011 10 31 40 14272-14283 The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Research Group Bethge Department Logothetis http://www.jneurosci.org/content/31/40/14272.full.pdf+html 10.1523/​JNEUROSCI.2539-11.2011 aeckerASEcker berensPBerens atoliasASTolias mbethgeMBethge article BerensEGTB2011 Reassessing optimal neural population codes with neurometric functions Proceedings of the National Academy of Sciences of the United States of America 2011 3 108 11 4423-4428 Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool—Fisher information—can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and a reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis http://www.pnas.org/content/108/11/4423.full.pdf+html 10.1073/pnas.1015904108 berensPBerens aeckerASEcker sgerwinnSGerwinn atoliasASTolias mbethgeMBethge article 6257 Decorrelated Neuronal Firing in Cortical Microcircuits Science 2010 1 327 5965 584-587 Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and to share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multitetrode arrays offering unprecedented recording quality to reexamine this question in the primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. Our findings suggest a refinement of current models of cortical microcircuit architecture and function: Either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.sciencemag.org/cgi/reprint/327/5965/584.pdf Biologische Kybernetik Max-Planck-Gesellschaft en 10.1126/science.1179867 aeckerASEcker berensPBerens georgeGAKeliris mbethgeMBethge nikosNKLogothetis atoliasASTolias article 5157 Generating Spike Trains with Specified Correlation Coefficients Neural Computation 2009 2 21 2 397-423 Spike trains recorded from populations of neurons can exhibit substantial pairwise correlations between neurons and rich temporal structure. Thus, for the realistic simulation and analysis of neural systems, it is essential to have efficient methods for generating artificial spike trains with specified correlation structure. Here we show how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model. Sampling from the model is computationally very efficient and, in particular, feasible even for large populations of neurons. The entropy of the model is close to the theoretical maximum for a wide range of parameters. In addition, this framework naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions. http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/macke2009_5157[0].pdf http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Research Group Bethge http://www.mitpressjournals.org/doi/pdf/10.1162/neco.2008.02-08-713 Biologische Kybernetik Max-Planck-Gesellschaft en 10.1162/neco.2008.02-08-713 jakobJHMacke berensPBerens aeckerASEcker atoliasASTolias mbethgeMBethge article 5614 Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex Frontiers in Neuroscience 2008 12 2 2 199-207 Extra-cellular voltage fluctuations (local field potentials; LFPs) reflecting neural mass action are ubiquitous across species and brain regions. Numerous studies have characterized the properties of LFP signals in the cortex to study sensory and motor computations as well as cognitive processes like attention, perception and memory. In addition, its extracranial counterpart – the electroencelphalogram (EEG) – is widely used in clinical applications. However, the link between LFP signals and the underlying activity of local populations of neurons remains largely elusive. Here, we review recent work elucidating the relationship between spiking activity of local neural populations and LFP signals. We focus on oscillations in the gamma-band (30-90Hz) of the local field potential in the primary visual cortex (V1) of the macaque that dominate during visual stimulation. Given that in area V1 much is known about the properties of single neurons and the cortical architecture, it provides an excellent opportunity to study the mechanisms underlying the generation of the local field potential. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://frontiersin.org/neuroscience/paper/10.3389/neuro.01/037.2008/pdf/ Biologische Kybernetik Max-Planck-Gesellschaft en 10.3389/neuro.01.037.2008 berensPBerens georgeGAKeliris aeckerASEcker nikosNKLogothetis atoliasASTolias article 5205 Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex Frontiers in Systems Neuroscience 2008 6 2 2 1-11 The local field potential (LFP), comprised of low-frequency extra-cellular voltage fluctuations, has been used extensively to study the mechanisms of brain function. In particular, oscillations in the gamma-band (30–90 Hz) are ubiquitous in the cortex of many species during various cognitive processes. Surprisingly little is known about the underlying biophysical processes generating this signal. Here, we examine the relationship of the local field potential to the activity of localized populations of neurons by simultaneously recording spiking activity and LFP from the primary visual cortex (V1) of awake, behaving macaques. The spatial organization of orientation tuning and ocular dominance in this area provides an excellent opportunity to study this question, because orientation tuning is organized at a scale around one order of magnitude finer than the size of ocular dominance columns. While we find a surprisingly weak correlation between the preferred orientation of multi-unit activity and gamma-band LFP recorded on the same tetrode, there is a strong correlation between the ocular preferences of both signals. Given the spatial arrangement of orientation tuning and ocular dominance, this leads us to conclude that the gamma-band of the LFP seems to sample an area considerably larger than orientation columns. Rather, its spatial resolution lies at the scale of ocular dominance columns. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.frontiersin.org/systemsneuroscience/paper/10.3389/neuro.06/002.2008/pdf/ Biologische Kybernetik Max-Planck-Gesellschaft en 10.3389/neuro.06.002.2008 berensPBerens georgeGAKeliris aeckerASEcker nikosNKLogothetis atoliasASTolias article 4788 Recording Chronically from the same Neurons in Awake, Behaving Primates Journal of Neurophysiology 2007 12 98 6 3780-3790 Understanding the mechanisms of learning requires characterizing how the response properties of individual neurons and interactions across populations of neurons change over time. In order to study learning in-vivo, we need the ability to track an electrophysiological signature that uniquely identifies each recorded neuron for extended periods of time. We have identified such an extracellular signature using a statistical framework which allows quantification of the accuracy by which stable neurons can be identified across successive recording sessions. Our statistical framework uses spike waveform information recorded on a tetrode’s four channels in order to define a measure of similarity between neurons recorded across time. We use this framework to quantitatively demonstrate for the first time the ability to record from the same neurons across multiple consecutive days and weeks. The chronic recording techniques and methods of analyses we report can be used to characterize the changes in brain circuits du e to learning. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://jn.physiology.org/cgi/reprint/00260.2007v1 Biologische Kybernetik Max-Planck-Gesellschaft en 10.1152/jn.00260.2007 atoliasASTolias aeckerASEcker AGSiapas hoenselaarAHoenselaar georgeGAKeliris nikosNKLogothetis inproceedings GatysEB2016_2 Image Style Transfer Using Convolutional Neural Networks 2016 6 2414-2423 Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well-known artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7780634 IEEE
Piscataway, NJ, USA
Las Vegas, NV, USA IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) 978-146738851-1 10.1109/CVPR.2016.265 LAGatys aeckerASEcker mbethgeMBethge
inproceedings GatysEB2015 Texture Synthesis Using Convolutional Neural Networks 2016 262-270 Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. The model provides a new tool to generate stimuli for neuroscience and might offer insights into the deep representations learned by convolutional neural networks. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis http://papers.nips.cc/paper/5633-texture-synthesis-using-convolutional-neural-networks Cortes, C. , N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett, R. Garnett Curran
Red Hook, NY, USA
Advances in Neural Information Processing Systems 28 Montréal, Canada Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015) LAGatys aeckerASEcker mbethgeMBethge
inproceedings 6076 Neurometric function analysis of population codes 2010 4 90-98 The relative merits of different population coding schemes have mostly been analyzed in the framework of stimulus reconstruction using Fisher Information. Here, we consider the case of stimulus discrimination in a two alternative forced choice paradigm and compute neurometric functions in terms of the minimal discrimination error and the Jensen-Shannon information to study neural population codes. We first explore the relationship between minimum discrimination error, Jensen-Shannon Information and Fisher Information and show that the discrimination framework is more informative about the coding accuracy than Fisher Information as it defines an error for any pair of possible stimuli. In particular, it includes Fisher Information as a special case. Second, we use the framework to study population codes of angular variables. Specifically, we assess the impact of different noise correlations structures on coding accuracy in long versus short decoding time windows. That is, for long time window we use the common Gaussian noise approximation. To address the case of short time windows we analyze the Ising model with identical noise correlation structure. In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding. http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/berens2009b_6076[0].pdf http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge http://nips.cc/Conferences/2009/ Bengio, Y. , D. Schuurmans, J. Lafferty, C. Williams, A. Culotta Curran
Red Hook, NY, USA
Advances in Neural Information Processing Systems 22 Biologische Kybernetik Max-Planck-Gesellschaft Vancouver, BC, Canada 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009) en 978-1-615-67911-9 berensPBerens sgerwinnSGerwinn aeckerASEcker mbethgeMBethge
poster CadenaEDWTB2017 A goal-driven deep learning approach for V1 system identification 2017 2 23 74 Understanding sensory processing in the visual system results from accurate predictions of its neural responses to arbitrary stimuli. Despite great efforts over the last decades, we still lack a full characterization of the computations in primary visual cortex (V1) and their role in higher cognitive functional tasks (e.g. object recognition). Recent goal-driven deep learning models have provided unprecedented predictive performance on the visual ventral stream and revealed a hierarchical correspondence. However, we still have to assess if their learned representations can also be used to predict single cell responses in V1. Here, we leverage these learned representations to build a model that predicts responses to natural images across layers of monkey V1. We use the internal representations of a high-performing convolutional neural network (CNN) trained on object recognition as a non-linear feature space for a Generalized Linear Model. We found that intermediate early layers in the CNN provided the best predictive performance on held out data. Our model significantly outperformed classical and current state-of-the-art methods on V1 identification. When exploring the properties of the best predictive layers in the CNN, we found striking similarities with known V1 computation. Our model is not only interpretable, but also interpolates between recent subunit-based hierarchical models and goal-driven deep learning models, leading to results that argue in favor of shared representations in the brain. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.cosyne.org/c/index.php?title=Cosyne2017_posters_1 Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2017) SCadena aeckerAEcker GDenfield EWalker atoliasATolias mbethgeMBethge poster DenfieldET2017 The Role of Internal Signals in Structuring V1 Population Activity 2017 2 31 Neuronal responses to repeated presentations of identical visual stimuli are variable. The source of this variability is unknown, but it is commonly treated as noise. We argue that this variability reflects, and is due to, computations internal to the brain. Relatively little research has examined the effect on neuronal responses of fluctuations in internal signals such as cortical state and attention, leaving a number of uncontrolled parameters that may contribute to neuronal variability. Attention increases neuronal response gain in a spatial and feature selective manner. We hypothesize that fluctuations in the strength and focus of attention are a major source of neuronal response variability and covariability. We first examine a simple model of a gain-modulating signal acting on a population of neurons and show that fluctuations in attention can increase individual and shared variability. To test our model’s predictions experimentally, we devised a cued-spatial attention, change-detection task to induce varying degrees of fluctuation in the subject’s attentional signal. We use multi-electrode recordings in primary visual cortex of macaques performing this task. We demonstrate that attention gain-modulates responses of V1 neurons in a manner consistent with results from higher-order areas. Our results also indicate neuronal covariability is elevated in conditions in which attention fluctuates. Overall, our results suggest that attentional fluctuations are an important contributor to neuronal variability and open the door to the use of statistical methods for inferring the state of these signals on behaviorally relevant timescales. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis https://media.bcm.edu/documents/2017/ec/abstract-book-complete.pdf Galveston, TX, USA 27th Annual Rush and Helen Record Neuroscience Forum GHDenfield aeckerAEcker atoliasATolias poster CadenaEDWTB2016 A goal-driven deep learning approach for V1 system identification 2016 9 21 40-41 Understanding sensory processing in the visual system results from accurate predictions of its neural responses to any kind of stimulus. Although great effort has been devoted to the task, we still lack a full characterization of primary visual cortex (V1) computations and their role in higher cognitive functional tasks (e.g. object recognition) in response to naturalistic stimuli. While previous goal-driven deep learning models have provided unprecedented performance on visual ventral stream predictions and revealed hierarchical correspondence, no study has used the representations learned by those models to predict single cell spike counts in V1. We introduce a novel model (Fig. 1A) that leverages these learned representations to build a linearized model with Poisson noise. We separately use the representations of each convolutional layer of a near-state of the art convolutional neural network (CNN) trained on object recognition to fit a model that predicts V1 responses to naturalistic stimuli. When fitted to data collected from neurons across cortical layers in V1 from an awake, fixating monkey, we found that, as we expected, intermediate early layers in the CNN provided better performance on held out data (Fig. 1B). Additionally we show that, using the best predictive layers, our model significantly outperforms classical and current state-of-the-art methods on V1 identification (Fig. 1C). When exploring the properties of the best predictive layers in the CNN, we found striking similarities with known V1 computation. Our model is not only interpretable, but also interpolates between recent subunit-based hierarchical models and goal-driven deep learning models leading to results that argue in favor of shared representations in the brain. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de https://abstracts.g-node.org/conference/BC16/abstracts#/uuid/d1f4edcc-aa10-43ec-9b49-588e3e884e8e Berlin, Germany Bernstein Conference 2016 10.12751/nncn.bc2016.0029 SACadena aeckerASEcker GHDenfield EYWalker atoliasASTolias mbethgeMBethge poster CadwellJSBFYFECT2016 Cell Lineage Directs teh Precise Assembly of Excitatory Neocortical Circuits 2016 6 60 The neocortex carries out complex mental processes such as perception and cognition through the interactions of billions of neurons connected by trillions of synapses. Recent studies suggest that excitatory cortical neurons with a shared developmental lineage are more likely to be synaptically connected to each other than to nearby, unrelated neurons [1, 2]. However, the precise wiring diagram between clonally related neurons is unknown, and the impact of cell lineage on neural computation remains controversial. Here we show that vertical connections linking neurons across cortical layers are specifically enhanced between clonally related neurons (Fig. 1). In contrast, lateral connections within a cortical layer preferentially occur between unrelated neurons (Fig. 1). Importantly, we observed these connection biases for distantly related cousin cells, suggesting that cell lineage influences a larger fraction of connections than previously thought. A simple quantitative model of cortical connectivity based on our empirically measured connection probabilities reveals that both increased vertical connectivity and decreased lateral connectivity between cousins promote the convergence of shared input onto clonally related neurons, providing a novel circuit-level mechanism by which clonal units form functional cell assemblies with similar tuning properties [3, 4]. Taken together, our data suggest that the integration of feedforward, intra-columnar input with lateral, inter-columnar information may represent a fundamental principle of cortical computation that is established, at least initially, by developmental programs. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://areadne.org/2016/pezaris-hatsopoulos-2016-areadne.pdf Santorini, Greece AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles CRCadwell XJiang fabeeFHSinz berensPBerens PGFahey DYatsenko EFroudarakis aeckerASEcker RJCotton atoliasASTolias poster DenfieldEBT2016 Correlated Variability in Population Activity: Noise or Signature of Internal Computations 2016 6 63 Neuronal responses to repeated presentations of identical visual stimuli are variable. The source of this variability is unknown, but it is commonly treated as noise and seen as an obstacle to understanding neuronal activity. We argue that this variability is not noise but reflects, and is due to, computations internal to the brain. Internal signals such as cortical state or attention interact with sensory information processing in early sensory areas. However, little research has examined the effect of fluctuations in these signals on neuronal responses, leaving a number of uncontrolled parameters that may contribute to neuronal variability. One such variable is attention, which increases neuronal response gain in a spatial and feature selective manner. Both the strength of this modulation and the focus of attention are likely to vary from trial to trial, and we hypothesize that these fluctuations are a major source of neuronal response variability and covariability. We first examine a simple model of a gain-modulating signal acting on a population of neurons and show that fluctuations in attention can increase individual and shared variability and generate a variety of correlation structures relevant to population coding, including limited range and differential correlations. To test our model’s predictions experimentally, we devised a cued-spatial attention, change-detection task to induce varying degrees of fluctuation in the subject’s attentional signal by changing whether the subject must attend to one stimulus location while ignoring another, or attempt to attend to multiple locations simultaneously. We use multi-electrode recordings with laminar probes in primary visual cortex of macaques performing this task. We demonstrate that attention gain-modulates responses of V1 neurons in a manner consistent with results from higher-order areas. Consistent with our model’s predictions, our preliminary results indicate neuronal covariability is elevated in conditions in which attention fluctuates and that neurons are nearly independent when attention is focused. Overall, our results suggest that attentional fluctuations are an important contributor to neuronal variability and open the door to the use of statistical methods for inferring the state of these signals on behaviorally relevant timescales. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://areadne.org/2016/pezaris-hatsopoulos-2016-areadne.pdf Santorini, Greece AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles GHDenfield aeckerASEcker mbethgeMBethge atoliasASTolias poster ReimerYEWSBHCST2016 DataJoint: Managing Big Scientific Data Using Matlab or Python 2016 6 99 The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and organized in a flexible way that allows swift exploration and analysis. Data management should guarantee consistency of intermediate results in subsequent multi-step processing pipelines such that changes in one part automatically propagate to all downstream results. Finally, data organization should be self-documenting to ensure that lab members and collaborators can access the data with minimal effort, even years after it was collected. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. While the challenges associated with large, complex data sets may be new to biologists, they have been addressed quite successfully in other contexts by relational databases, which provide a principled approach for effective data management. DataJoint is an open-source framework that provides a clean implementation of core concepts of the relational data model to facilitate multi-user access, effcient queries, distributed computing, and cascading dependencies across multiple data modalities. Critically, while DataJoint relies on an established relational database management system (MySQL) as its backend, data access and manipulation are performed transparently in the commonly-used languages MATLAB or Python, and DataJoint can be integrated into new and existing analyses written in these languages with minimal effort or additional training. DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://areadne.org/2016/pezaris-hatsopoulos-2016-areadne.pdf Santorini, Greece AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles JReimer DYatsenko aeckerAEcker EYWalker fabeeFSinz berensPBerens AHoenselaar RJCotton AGSiapas atoliasASTolias poster GatysEB2016 A Neural Algorithm of Artistic Style Journal of Vision 2016 5 14 16 12 326 In fine art, especially painting, humans have mastered the skill to create unique visual experiences by composing a complex interplay between the content and style of an image. The algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. Recently, a class of biologically inspired vision models called Deep Neural Networks have demonstrated near-human performance in complex visual tasks such as object and face recognition. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system can separate and recombine the content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. In light of recent studies using fMRI and electrophysiology that have shown striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path towards an algorithmic understanding of how humans create and perceive artistic imagery. The algorithm introduces a novel class of stimuli that could be used to test specific computational hypotheses about the perceptual processing of artistic style. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://jov.arvojournals.org/article.aspx?articleid=2550311 St. Pete Beach, FL, USA 16th Annual Meeting of the Vision Sciences Society (VSS 2016) 10.1167/16.12.326 LAGatys aeckerASEcker mbethgeMBethge poster WallisEGFWB2016 Seeking summary statistics that match peripheral visual appearance using naturalistic textures generated by Deep Neural Networks Journal of Vision 2016 5 14 16 12 230 An important hypothesis that emerged from crowding research is that the perception of image structure in the periphery is texture-like. We investigate this hypothesis by measuring perceptual properties of a family of naturalistic textures generated using Deep Neural Networks (DNNs), a class of algorithms that can identify objects in images with near-human performance. DNNs function by stacking repeated convolutional operations in a layered feedforward hierarchy. Our group has recently shown how to generate shift-invariant textures that reproduce the statistical structure of natural images increasingly well, by matching the DNN representation at an increasing number of layers. Here, observers discriminated original photographic images from DNN-synthesised images in a spatial oddity paradigm. In this paradigm, low psychophysical performance means that the model is good at matching the appearance of the original scenes. For photographs of natural textures (a subset of the MIT VisTex dataset), discrimination performance decreased as the DNN representations were matched to higher convolutional layers. For photographs of natural scenes (containing inhomogeneous structure), discrimination performance was nearly perfect until the highest layers were matched, whereby performance declined (but never to chance). Performance was only weakly related to retinal eccentricity (from 1.5 to 10 degrees) and strongly depended on individual source images (some images were always hard, others always easy). Surprisingly, performance showed little relationship to size: within a layer-matching condition, images further from the fovea were somewhat harder to discriminate but this result was invariant to a three-fold change in image size (changed via up/down sampling). The DNN stimuli we examine here can match texture appearance but are not yet sufficient to match the peripheral appearance of inhomogeneous scenes. In the future, we can leverage the flexibility of DNN texture synthesis for testing different sets of summary statistics to further refine what information can be discarded without affecting appearance. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://jov.arvojournals.org/article.aspx?articleid=2550215 St. Pete Beach, FL, USA 16th Annual Meeting of the Vision Sciences Society (VSS 2016) 10.1167/16.12.230 TSAWallis aeckerASEcker LAGatys CMFunke felixFAWichmann mbethgeMBethge poster YatsenkoFERJT2016 Strong functional connectivity of parvalbumin-expressing cortical interneurons 2016 2 27 221 The morphological and electrophysiological properties of parvalbumin-expressing inhibitory interneurons (PV+ neurons) suggest their role as synchronizers and normalizers of the local cortical microcircuit. PV+ cells are thought to average the local activity and dynamically regulate its overall level. In apparent agreement with this model, previous studies have shown stable patterns of correlations of the spiking activity of the PV+ neurons among themselves and with the local excitatory cells. However, we have previously shown that, in sufficiently dense recordings, estimates of the partial pairwise correlations of the spiking activity can yield a more insightful picture of interactions in the circuit, or its functional connectivity. Using high-speed 3D two-photon imaging of calcium signals and genetically encoded fluorescent markers of PV+ neurons, we recorded the activity of the majority of neurons in 200 um x 200 um x 100 um volumes in layers 2/3 and 4 of mouse visual cortex during visual stimulation. If PV+ neurons simply pooled the activity of the local circuit, their activity would be predicted from the local circuit and the partial correlations among the PV+ neurons would all but vanish. Surprisingly, we found that the partial pairwise correlations among the PV+ cells were exceptionally high. In fact, the partial pairwise correlations enhanced the differentiation of PV+ neurons from other cell types. The average partial pairwise correlation between PV+/PV+ pairs was 4.9 times higher than between PV-/PV- pairs whereas the average noise correlations differed by the factor of 1.5. This effect was insensitive to the choice of the temporal scales of correlation analysis. Although other explanations cannot yet be excluded, the present finding may suggest that the correlations among the PV+ neurons are shaped predominantly by structured input from outside the local circuit such as, for example, by input from layer 5. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.cosyne.org/c/index.php?title=Cosyne_16 Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2016) DYatsenko EFroudarakis aeckerAEcker RRosenbaum KJosic atoliasATolias poster CottonEFBBST2015 Scaling of information in large sensory neuronal populations 2015 10 19 45 331.01 Individual neurons are noisy. Therefore, it seems necessary to pool the activity of many neurons to obtain an accurate representation of the environment. However, it is widely believed that shared noise in the activity of nearby neurons renders such pooling ineffective, limiting the accuracy of the population code and, ultimately, behavior. However, these predictions are based on extrapolating models fit to small numbers of neurons and have not been tested experimentally. Using a novel high-speed 3D-microscope we densely recorded from hundreds of neurons in the mouse visual cortex and measured the amount of information encoded. We find that the information in this sensory population increases approximately linearly with population size and does not saturate, even for several hundred neurons. This information growth is facilitated by a correlation structure that is not aligned with the tuning, making it less harmful than would be predicted from pairwise measurements. Accordingly, a decoder that accounts for the correlation structure outperforms one that does not. Our findings suggest that sensory representations may be more accurate than previously thought and therefore that psychophysical limitations may arise from downstream neural processes rather than limitations in the sensory encoding. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.sfn.org/am2015/ Chicago, IL, USA 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015) JRCotton aeckerASEcker EFroudarakis berensPBerens mbethgeMBethge PSaggau atoliasASTolias poster CadwellJBFYFET2015 Sibling rivalry and cooperation among excitatory neurons in the neocortex 2015 10 17 45 59.13 The mammalian neocortex carries out complex mental processes such as cognition, perception and decision-making through the interactions of billions of neurons connected by trillions of synapses. We are just beginning to understand how networks of neurons become wired together during development to give rise to cortical computations. Recent studies have shown that excitatory cortical neurons with a shared ontogenetic lineage form vertical columns spanning multiple cortical layers and that these “sister cells” are more likely to be synaptically connected to each other than to nearby, unrelated neurons. However, the precise wiring diagram between sister cells is unknown. Here we show that connectivity between sister cells depends on the laminar position of the pre- and post-synaptic neurons. In contrast to previous studies, we find that although sister cells residing in different cortical layers are more likely to be connected, sister cells located within the same layer are less likely to be connected to each other compared to distance-matched controls. Avoidance of cells that receive common input may be a fundamental principle of information processing within a cortical column. Our findings challenge the prevailing hypothesis that shared developmental lineage is always associated with an increase in connectivity, and suggest that both attraction and repulsion play an important role in shaping cortical circuits. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.sfn.org/am2015/ Chicago, IL, USA 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015) CRCadwell XJiang berensPBerens PGFahey DYatsenko EFroudarakis aeckerASEcker atoliasASTolias poster BassettoSEM2015 A statistical characterization of neural population responses in V1 2015 9 16 146-147 Population activity in primary visual cortex exhibits substantial variability that is correlated on multiple time scales and across neurons [1]. A quantitative account of how visual information is encoded in population of neurons in primary visual cortex therefore requires an accurate characterization of this variability. Our goal is provide a statistical model for capturing the statistical structure of this variability and its dependence on external stimuli, with particular focus on temporal correlations both on short (withintrial) and long (across-trial) time-scales [2]. We address this question using neural population recordings from primary visual cortex in response to drifting gratings [3], using the framework of generalized linear models (GLMs). To model stimulus-driven responses, we take a non-parametric approach and employ Gaussian-process priors to model the smoothness of response-profiles across time and different stimulus orientations, and low-rank constraints to facilitate inference from limited data. We find that the parameters which control the prior smoothness are consistent across neurons within each recording session, but differ markedly across recordings. For most neurons, the time-varying response across all stimulus orientations can be well captured using a lowrank decomposition with k = 4 dimensions. To capture slow modulations in firing rates, we include covariates in the GLM which are constrained to vary smoothly across trials, and find that including these terms leads to significant improvements in goodness-of-fit. Finally, we use latent dynamical systems [3] with point-process observation models [4] to capture variations and co-variations in firing rates on fast time-scales. While we focus our analysis on modelling neural population responses in V1, our approach provides a general formalism for obtaining an accurate quantitative model of response variability in neural populations. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Macke Department Logothetis http://www.nncn.de/de/bernstein-conference/2015/program Heidelberg, Germany Bernstein Conference 2015 10.12751/nncn.bc2015.0139 gbassettoGBassetto fsandhaegerFSandhaeger aeckerAEcker jakobJHMacke poster EckerDTB2015 On the structure of population activity under fluctuations in attentional state 2015 9 16 185 Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron’s gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations – even if unknown to a downstream readout – do not impair the readout accuracy despite inducing limited-range correlations. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.nncn.de/de/bernstein-conference/2015/program Heidelberg, Germany Bernstein Conference 2015 10.12751/nncn.bc2015.0179 aeckerASEcker GHDenfield atoliasASTolias mbethgeMBethge poster GatysEB2015_2 Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks 2015 9 16 219 It is a long standing question how biological systems transform visual inputs to robustly infer high level visual information. Research in the last decades has established that much of the underlying computations take place in a hierarchical fashion along the ventral visual pathway. However, the exact processing stages along this hierarchy are difficult to characterise. Here we present a method to generate stimuli that will allow a principled description of the processing stages along the ventral stream. We introduce a new parametric texture model based on the powerful feature spaces of convolutional neural networks optimised for object recognition. We show that constraining spatial summary statistic on feature maps suffices to synthesise high quality natural textures. Moreover we establish that our texture representations continuously disentangle high level visual information and demonstrate that the hierarchical parameterisation of the texture model naturally enables us to generate novel types of stimuli for systematically probing mid-level vision. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.nncn.de/de/bernstein-conference/2015/program Heidelberg, Germany Bernstein Conference 2015 10.12751/nncn.bc2015.0220 LAGatys aeckerASEcker mbethgeMBethge poster FroudarakisBECSYSBT2014_2 Population Code in Mouse V1 Facilities Read-out of Natural Scenes through Increased Sparseness 2014 6 69 The neural code is believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher-order correlations in natural scenes induce a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read-out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active, awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://areadne.org/2014/home.html Santorini, Greece AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles AFroudarakis berensPBerens aeckerASEcker RJCotton fabeeFHSinz DYatsenko PSaggau mbethgeMBethge atoliasASTolias poster CottonFEBST2014 Scaling of Information in Large Sensory Neuronal Populations 2014 6 60 Although we know a lot about how individual neurons in the brain represent the sensory environment, we are far from understanding how neural populations represent sensory information. Because individual neurons are noisy, pooling the activity of many neurons with similar response properties seems necessary to obtain an accurate representation of the sensory environment. However, it is widely believed that shared noise (or, noise correlations) in the activity of nearby neurons renders such pooling ineffective, profoundly limiting the accuracy of any population code and, ultimately, behavior. This belief is based on model-based extrapolations from correlations measured in individual pairs of neurons, as it has been impossible to record simultaneously from complete neuronal populations. Here, we use a novel 3D high-speed in vivo two-photon microscope to record nearly all of the hundreds of neurons in a small volume of the mouse primary visual cortex and directly measure the amount of information encoded by these local populations. In contrast to previous predictions, we find that the information in a sensory population increases approximately linearly with population size and does not saturate even for several hundred neurons. Moreover, even a decoder ignoring correlations between neurons can decode 80% of the information in the population. Our results suggest that sensory neural populations represent information in a truly distributed manner and pooling of neural activity within local circuits is much more effective than previously anticipated. Thus, the representation in early sensory areas does not appear to be impaired substantially by shared sensory noise and limitations in behavioral performance in psychophysical tasks may need to be attributed to processes downstream of the sensory population. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://areadne.org/2014/home.html Santorini, Greece AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles RJCotton EFroudarakis aeckerASEcker berensPBerens PSaggau atoliasASTolias poster EckerBCSDCSBT2014_2 State Dependence of Noise Correlation in Macaque Primary Visual Cortex 2014 6 64 Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1–2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations, but can also be estimated and accounted for based on neuronal population activity. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://areadne.org/2014/home.html Santorini, Greece AREADNE 2014: Research in Encoding and Decoding of Neural Ensembles aeckerASEcker berensPBerens RJCotton MSubramaniyan GHDenfield CRCadwell ssmirnakisSMSmirnakis mbethgeMBethge atoliasASTolias poster GatysETB2013 Information Coding in the Variance of Neural Activity 2013 9 44 Neural activity in the cortex appears to be notoriously noisy. A widely accepted explanation for this finding is that excitatory and inhibitory inputs to downstream neurons are balanced in a way that the upstream population activity does not affect the mean but only the variance of the input current. This can be thought of as a multiplicative noise channel. However, the capacity limits imposed by this information channel are not known. Here we develop a general understanding of the encoding process in terms of scale mixture processes and derive information-theoretic bounds on their performance. Our results show that signal transmission via instantaneous changes in the variance can behave quite differently from the common additive noise channel. We perform systematic numerical analyses to maximize the information across the variance channel and thus obtain tight lower bounds to its capacity. Furthermore, we found that additional noise, resembling the unreliable synaptic transmission of spikes, can surprisingly enhance the coding performance of the channel. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Schölkopf https://portal.g-node.org/abstracts/bc13/#/doi/nncn.bc2013.0020 Tübingen, Germany Bernstein Conference 2013 10.12751/nncn.bc2013.0020 LGatys aeckerAEcker TTchumatchenko mbethgeMBethge poster FroudarakisBCESBT2013 Encoding of natural scene statistics in the primary visual cortex of the mouse 2013 3 II-76 http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge http://www.cosyne.org/c/index.php?title=Cosyne_13 Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2013) EFroudarakis berensPBerens JRCotton aeckerASEcker PSaggau mbethgeMBethge atoliasATolias poster EckerBTB2012_2 The correlation structure induced by fluctuations in attention 2012 6 56 How attention shapes the structure of population activity has attracted substantial interest over the past decades. Attention has traditionally been associated with an increase in firing rates, reflecting a change in the gain of the population. More recent studies also report a change in noise correlations, which is thought to reflect changes in functional connectivity. However, since the degree of attention can vary substantially from trial to trial even within one experimental condition, the measured correlations could actually reflect fluctuations in the attention-related feedback signal (gain) rather than feed-forward noise, as often assumed. To gain insights into this issue we analytically analyzed the standard model of spatial attention, where directing attention to the receptive field of a neuron increases its response gain. We assumed conditionally independent neurons (no noise correlations) and asked how uncontrolled fluctuations in attention affect the correlation structure. First, we found that this simple model of spatial attention explains the empirically measured correlation structure quite well. In addition to a positive average level of correlations, it predicts both an increase in correlations with firing rates, as observed in many studies, and a decrease in correlations with the difference of two neurons’ tuning functions — a structure generally referred to as limited range correlations. Second, we asked how fluctuations in attention would affect the accuracy of a population code, if treated as noise by a downstream readout. Based on previous theoretical results, it would be expected that they negatively affect readout accuracy because of the limited range correlations they induce. Surprisingly, we found that this is not the case: correlations due to random gain fluctuations do not affect readout accuracy because their major axis is orthogonal to changes in the stimulus orientation. Our results can be readily generalized to include feature-based attention. The model has very few free parameters and can potentially account for a large fraction of the experimentally observed spike count (co-)variance. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Department Schölkopf http://areadne.org/2012/home.html Santorini, Greece AREADNE 2012: Research in Encoding and Decoding of Neural Ensembles aeckerASEcker berensPBerens atoliasASTolias mbethgeMBethge poster EckerBTB2012 The correlation structure induced by fluctuations in attention 2012 2 9 180 Attention has traditionally been associated with an increase in firing rates, reflecting a change in the gain of the population. More recent studies also report a change in noise correlations, which is thought to reflect changes in functional connectivity. However, since the degree of attention can vary substantially from trial to trial even within one experimental condition, the measured correlations could actually reflect fluctuations in the attentionrelated feedback signal (gain) rather than feed-forward noise, as often assumed. To gain insights into this issue we analytically analyzed the standard model of spatial attention, where directing attention to the receptive field of a neuron increases its response gain. We assumed conditionally independent neurons (no noise correlations) and asked how uncontrolled fluctuations in attention affect the correlation structure. First, we found that this simple model of spatial attention explains the empirically measured correlation structure quite well. In addition to a positive average level of correlations, it predicts both an increase in correlations with firing rates, as observed in many studies, and a decrease in correlations with the difference of two neurons’ tuning functions—a structure generally referred to as limited range correlations. Second, we asked how fluctuations in attention would affect the accuracy of a population code, if treated as noise by a downstream readout. Based on previous theoretical results, it would be expected that they negatively affect readout accuracy because of the limited range correlations they induce. Surprisingly, we found that this is not the case: correlations due to random gain fluctuations do not affect readout accuracy because their major axis is orthogonal to changes in the stimulus orientation. Our results can be readily generalized to include feature-based attention. The model has very few free parameters and can potentially account for a large fraction of the observed spike count (co-)variance. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Schölkopf http://www.cosyne.org/c/index.php?title=Cosyne_12 Salt Lake City, UT, USA 9th Annual Computational and Systems Neuroscience Meeting (Cosyne 2012) aeckerAEcker berensPBerens atoliasATolias mbethgeMBethge poster BerensEGTB2011_2 Optimal Population Coding, Revisited 2011 2 III-67 Cortical circuits perform computations within few dozens of milliseconds with each neuron emitting only a few spikes. In this regime conclusions based on Fisher information, which is commonly used to assess the quality of population codes, are not always valid. Here we revisit the effect of tuning function width and correlation structure on neural population codes for angular variables using ideal observer analysis in both reconstruction and classification tasks employing Monte-Carlo simulations and analytical derivations. We show that the optimal tuning width of individual neurons and the optimal correlation structure of the population depend on the signal-to-noise ratio for both the reconstruction and the classification task. Strikingly, both ideal observers lead to very similar conclusions at low signal-to-noise ratio. In contrast, Fisher information favors severely suboptimal coding schemes in this regime. To further investigate the coding properties of Fisher-optimal codes, we compute the full neurometric functions of an ideal observer in the stimulus discrimination task, which allows us to evaluate population codes separately for fine and coarse discrimination. We find that codes with Fisher-optimal tuning width show strikingly bad performance for simple coarse discrimination tasks with a ëpedestal errorí, which is independent of population size. We show analytically that this is a necessary consequence of the fact that in such codes only few neurons are activated by each stimulus, irrespective of the population size. Further we show that the initial region of the neurometric function goes to zero with increasing population size. As a consequence, the overall error achieved by Fisher-optimal ensembles saturates for large populations. In summary, based on exact ideal observer analysis for both stimulus reconstruction and discrimination tasks we obtained (1) an accurate assessment of neural population codes at all signal-to-noise ratios and (2) analytical insights into the suboptimal behavior of Fisher-optimal population codes. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Department Schölkopf http://www.cosyne.org/c/index.php?title=Cosyne_11_posters3 Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2011) berensPBerens aeckerASEcker sgerwinnSGerwinn atoliasASTolias mbethgeMBethge poster 7055 Decorrelated neuronal firing in cortical microcircuits 2010 11 40 73.20 Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multi-tetrode arrays offering unprecedented recording quality to re-examine this question in primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. In a total of 46 recording sessions from two monkeys, we presented either static or drifting sine-wave gratings at eight different orientations. We recorded from 407 well isolated, visually responsive and orientation-tuned neurons, resulting in 1907 simultaneously recorded pairs of neurons. In 406 of these pairs both neurons were recorded by the same tetrode. Despite being physically close to each other and having highly overlapping receptive fields, neurons recorded from the same tetrode had exceedingly low spike count correlations (rsc = 0.005 ± 0.004; mean ± SEM). Even cells with similar preferred orientations (rsignal > 0.5) had very weak correlations (rsc = 0.028 ± 0.010). This was also true if pairs were strongly driven by gratings with orientations close to the cells’ preferred orientations. Correlations between neurons recorded by different tetrodes showed a similar pattern. They were low on average (rsc = 0.010 ± 0.002) with a weak relation between tuning similarity and spike count correlations (two-sample t test, rsignal < 0.5 versus rsignal > 0.5: P = 0.003, n = 1907). To investigate whether low correlations also occur under more naturalistic stimulus conditions, we presented natural images to one of the monkeys. The average rsc was close to zero (rsc = 0.001 ± 0.005, n = 329) with no relation between receptive field overlap and spike count correlations. We obtained a similar result during stimulation with moving bars in a third monkey (rsc = 0.014 ± 0.011, n = 56). Our findings suggest a refinement of current models of cortical microcircuit architecture and function: either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.sfn.org/am2010/index.aspx?pagename=abstracts_main Biologische Kybernetik Max-Planck-Gesellschaft San Diego, CA, USA 40th Annual Meeting of the Society for Neuroscience (Neuroscience 2010) en aeckerASEcker berensPBerens georgeGAKeliris mbethgeMBethge nikosNKLogothetis atoliasASTolias poster 6810 Decorrelated Firing in Cortical Microcircuits 2010 6 2010 58 Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multi-tetrode arrays offering unprecedented recording quality to re-examine this question in primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. In a total of 46 recording sessions from two monkeys, we presented either static or drifting sine-wave gratings at eight different orientations. We recorded from 407 well isolated, visually responsive and orientation-tuned neurons, resulting in 1907 simultaneously recorded pairs of neurons. In 406 of these pairs both neurons were recorded by the same tetrode. Despite being physically close to each other and having highly overlapping receptive fields, neurons recorded from the same tetrode had exceedingly low spike count correlations (rsc = 0.005 ± 0.004; mean ± SEM). Even cells with similar preferred orientations (rsignal > 0.5) had very weak correlations (rsc = 0.028 ± 0.010). This was also true if pairs were strongly driven by gratings with orientations close to the cells’ preferred orientations. Correlations between neurons recorded by different tetrodes showed a similar pattern. They were low on average (rsc = 0.010 ± 0.002) with a weak relation between tuning similarity and spike count correlations (two-sample t test, rsignal < 0.5 versus rsignal > 0.5: P = 0.003, n = 1907). To investigate whether low correlations also occur under more naturalistic stimulus conditions, we presented natural images to one of the monkeys. The average rsc was close to zero (rsc = 0.001 ± 0.005, n = 329) with no relation between receptive field overlap and spike count correlations. We obtained a similar result during stimulation with moving bars in a third monkey (rsc = 0.014 ± 0.011, n = 56). Our findings suggest a refinement of current models of cortical microcircuit architecture and function: either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge http://www.areadne.org/2010/home.html Hatsopoulos, N. G., S. Pezaris Biologische Kybernetik Max-Planck-Gesellschaft Santorini, Greece AREADNE 2010: Research in Encoding And Decoding of Neural Ensembles en aeckerASEcker berensPBerens georgeGAKeliris mbethgeMBethge nikosNKLogothetis atoliasASTolias poster 5844 Sensory input statistics and network mechanisms in primate primary visual cortex Frontiers in Systems Neuroscience 2009 3 2009 Conference Abstracts: Computational and Systems Neuroscience Understanding the structure of multi-neuronal firing patterns in ensembles of cortical neurons is a major challenge for systems neuroscience. The dependence of network properties on the statistics of the sensory input can provide important insights into the computations performed by neural ensembles. Here, we study the functional properties of neural populations in the primary visual cortex of awake, behaving macaques by varying visual input statistics in a controlled way. Using arrays of chronically implanted tetrodes, we record simultaneously from up to thirty well-isolated neurons while presenting sets of images with three different correlation structures: spatially uncorrelated white noise (whn), images matching the second-order correlations of natural images (phs) and natural images including higher-order correlations (nat). We find that groups of six nearby cortical neurons show little redundancy in their firing patterns (represented as binary vectors, 10ms bins) but rather act almost independently (mean multi-information 0.85 bits/s, range 0.16 - 1.90 bits/s, mean fraction of marginal entropy 0.34 %, N=46). Although network correlations are weak, they are statistically significant. While relatively few groups showed significant redundancies under stimulation with white noise (67.4 ± 3.2%; mean fraction of groups ± S.E.M.), many more did so in the other two conditions (phs: 95.7 ± 0.6%; nat: 89.1 ± 1.4%). Additional higher-order correlations in natural images compared to phase scrambled images did not increase but rather decrease the redundancy in the cortical representation: Network correlations are significantly higher in phs than in nat, as is the number of significantly correlated groups. Multi-information measures the reduction in entropy due to any form of correlation. By using second order maximum entropy modeling, we find that a large fraction of multi-information is accounted for by pairwise correlations (whn: 75.0 ± 3.3%; phs: 82.8 ± 2.1%; nat: 80.8 ± 2.4%; groups with significant redundancy). Importantly, stimulation with natural images containing higher-order correlations only lead to a slight increase in the fraction of redundancy due to higher-order correlations in the cortical representation (mean difference 2.26 %, p=0.054, Sign test). While our results suggest that population activity in V1 may be modeled well using pairwise correlations only, they leave roughly 20-25 % of the multi-information unexplained. Therefore, choosing a particular form of higher-order interactions may improve model quality. Thus, in addition to the independent model, we evaluated the quality of three different models: (a) The second-order maximum entropy model, which minimizes higher-order correlations, (b) a model which assumes that correlations are a product of common inputs (Dichotomized Gaussian) and (c) a mixture model in which correlations are induced by a discrete number of latent states. We find that an independent model is sufficient for the white noise condition but neither for phs or nat. In contrast, all of the correlation models (a-c) perform similarly well for the conditions with correlated stimuli. Our results suggest that under natural stimulation redundancies in cortical neurons are relatively weak. Higher-order correlations in natural images do not increase but rather decrease the redundancies in the cortical representation. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge http://www.cosyne.org/c/index.php?title=Cosyne_09 Biologische Kybernetik Max-Planck-Gesellschaft Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2009) en 10.3389/conf.neuro.06.2009.03.298 berensPBerens jakobJHMacke aeckerASEcker RJCotton mbethgeMBethge atoliasASTolias poster 5359 Towards the neural basis of the flash-lag effect 2008 9 http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Research Group Bethge Biologische Kybernetik Max-Planck-Gesellschaft Delmenhorst, Germany International Workshop: Aspects of Adaptive Cortex Dynamics en aeckerASEcker berensPBerens hoenselaarAHoenselaar MSubramaniyan atoliasASTolias mbethgeMBethge poster MackeBEOTB2008 Modeling populations of spiking neurons with the Dichotomized Gaussian distribution 2008 7 http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis http://www.theswartzfoundation.org/summer-meeting-2008.asp Princeton, NJ, USA Annual Meeting 2008 of Sloan-Swartz Centers for Theoretical Neurobiology jakobJHMacke berensPBerens aeckerASEcker MOpper atoliasASTolias mbethgeMBethge poster 5101 Flexible Models for Population Spike Trains 2008 6 48 In order to understand how neural systems perform computations and process sensory information, we need to understand the structure of firing patterns in large populations of neurons. Spike trains recorded from populations of neurons can exhibit substantial pair wise correlations between neurons and rich temporal structure. Thus, efficient methods for generating artificial spike trains with specified correlation structure are essential for the realistic simulation and analysis of neural systems. Here we show how correlated binary spike trains can be modeled by means of a latent multivariate Gaussian model. Sampling from our model is computationally very efficient, and in particular, feasible even for large populations of neurons. We show empirically that the spike trains generated with this method have entropy close to the theoretical maximum. They are therefore consistent with specified pair-wise correlations without exhibiting systematic higher-order correlations. We compare our model to alternative approaches and discuss its limitations and advantages. In addition, we demonstrate its use for modeling temporal correlations in a neuron recorded in macaque primary visual cortex. Neural activity is often summarized by discarding the exact timing of spikes, and only counting the total number of spikes that a neuron (or population) fires in a given time window. In modeling studies, these spike counts have often been assumed to be Poisson distributed and neurons to be independent. However, correlations between spike counts have been reported in various visual areas. We show how both temporal and inter-neuron correlations shape the structure of spike counts, and how our model can be used to generate spike counts with arbitrary marginal distributions and correlation structure. We demonstrate its capabilities by modeling a population of simultaneously recorded neurons from the primary visual cortex of a macaque, and we show how a model with correlations accounts for the data far better than a model that assumes independence. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Research Group Bethge http://www.areadne.org/2008/home.html Biologische Kybernetik Max-Planck-Gesellschaft Santorini, Greece AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles en mbethgeMBethge jakobJHMacke berensPBerens aeckerASEcker atoliasASTolias poster 5100 Pairwise Correlations and Multineuronal Firing Patterns in the Primary Visual Cortex of the Awake, Behaving Macaque 2008 6 46 Understanding the structure of multi-neuronal firing patterns has been a central quest and major challenge for systems neuroscience. In particular, how do pairwise interactions between neurons shape the firing patterns of neuronal ensembles in the cortex? To study this question, we recorded simultaneously from multiple single neurons in the primary visual cortex of an awake, behaving macaque using an array of chronically implanted tetrodes1. High contrast flashed and moving bars were used for stimulation, while the monkey was required to maintain fixation. In a similar vein to recent studies of in vitro preparations 2,3,5, we applied maximum entropy analysis for the first time to the binary spiking patterns of populations of cortical neurons recorded in vivo from the awake macaque. We employed the Dichotomized Gaussian distribution, which can be seen as a close approximation to the pairwise maximum-entropy model for binary data4. Surprisingly, we find that even pairs of neurons with nearby receptive fields (receptive field center distance < 0.15°) have only weak correlations between their binary responses computed in bins of 10 ms (median absolute correlation coefficient: 0.014, 0.010-0.019, 95% confidence intervals, N=95 pairs; positive correlations: 0.015, N=59; negative correlations: -0.013, N=36). Accordingly, the distribution of spiking patterns of groups of 10 neurons is described well with a model that assumes independence between individual neurons (Jensen-Shannon-Divergence: 1.06×10-2 independent model, 0.96×10-2 approximate second-order maximum-entropy model4; H/H1=0.992). These results suggest that the distribution of firing patterns of small cortical networks in the awake animal is predominantly determined by the mean activity of the participating cells, not by their interactions. Meaningful computations, however, are performed by neuronal populations much larger than 10 neurons. Therefore, we investigated how weak pairwise correlations affect the firing patterns of artificial populations4 of up to 1000 cells with the same correlation structure as experimentally measured. We find that in neuronal ensembles of this size firing patterns with many active or silent neurons occur considerably more often than expected from a fully independent population (e.g. 130 or more out of 1000 neurons are active simultaneously roughly every 300 ms in the correlated model and only once every 3-4 seconds in the independent model). These results suggest that the firing patterns of cortical networks comparable in size to several minicolumns exhibit a rich structure, even if most pairs appear relatively independent when studying small subgroups thereof. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Research Group Bethge http://www.areadne.org/2008/home.html Biologische Kybernetik Max-Planck-Gesellschaft Santorini, Greece AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles en berensPBerens aeckerASEcker MSubramaniyan jakobJHMacke PHauck mbethgeMBethge atoliasASTolias poster 4591 On the spatial scale of the local field potential - orientation and ocularity tuning of the local field potential in the primary visual cortex of the macaque 2007 11 37 176.7 The local field potential (LFP) and, in particular, the gamma-band frequency range (30-90 Hz) have recently received much attention, as numerous studies have shown correlations between LFP and sensory, motor and cognitive variables in various cortical regions. However, the extent to which it reflects the activity of local populations of neurons remains elusive. The issue of spatial scale is central for understanding the origins of the LFP and how this signal can be used to study the functional organization of the brain. We addressed this question by simultaneously recording multi-unit spiking activity (MUA) and LFP from the primary visual cortex (V1) of awake, behaving macaques using arrays of tetrodes. Oriented gratings were used for visual stimulation, applied either binocular or monocular. The columnar organization of stimulus orientation and ocularity in V1 provides an excellent opportunity to study the spatial precision of the LFP signal, because neurons with similar orientation preference are organized at the fine spatial scale of cortical microcolumns (50-100 μm), whereas ocular dominance columns span around 450 μm. As shown before, we find that the increase of LFP gamma-band power is a function of orientation and ocularity of the stimulus. However, the power of the gamma-band contains much less information about the orientation of the stimulus than the MUA recorded at the same site. The average discriminability d' between preferred and orthogonal orientation was 2.46±0.15 for MUA and 1.01±0.05 for LFP (mean ±std). Moreover, we find only a weak correlation between the preferred orientation of the MUA tuning function and that of the LFP (r=0.21, p<0.05). In contrast, we find a strong correlation between the preferred ocularity of the two signals (r=0.53, p<1e-9). We therefore conclude that the gamma-power of the LFP does not reflect well the local activity on the scale of orientation columns but does capture the ocular dominance structure of V1. We suggest that gamma-band activity is generated by ensembles of neurons larger than 50-100 μm. In agreement with a previous study (Liu & Newsome, 2006) we find that it more likely resembles the activity of neurons from an area spanning a few hundred micrometers. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.sfn.org/am2007/ Biologische Kybernetik Max-Planck-Gesellschaft San Diego, CA, USA 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007) en berensPBerens aeckerASEcker georgeGAKeliris nikosNKLogothetis atoliasASTolias poster 4733 Recording chronically from the same neurons in awake, behaving primates 2007 11 37 176.8 Understanding the mechanisms of learning and memory consolidation requires characterizing how the response properties of individual neurons and interactions across populations of neurons change over time, during periods spanning multiple days. We used multiple chronically implanted tetrodes to record single unit activity from area V1 of the awake, behaving macaque and developed a method to quantitatively determine recording stability. Our method is based on a statistical framework which uses similarity of action potential waveforms to detect stable recordings given a pre-defined type I error rate. The similarity measure that was used takes into account both the shape of the action potential waveform and the amplitude ratio across channels, which depends on the location of the neuron relative to the tetrode. 271 well-isolated single units were recorded from 7 tetrodes during two periods of up to 23 days. We computed the distribution of pairwise similarities of average waveforms recorded on consecutive recording sessions during the first 34 days after implantation of the chronic drive. During this period, there was no recording stability due to regular adjustments of the tetrodes. We used this distribution as an empirical null distribution for hypothesis testing. Using this statistical procedure and a type I error rate of alpha = 0.05, we find that of all single units recorded on a given day, 51% could be recorded for at least 2 days, 40% for at least 3 days, and 25% for at least 7 days. In addition, we adapted a recently proposed multivariate statistical test (Gretton et al., 2007) to test whether the waveforms obtained at consecutive days come from the same underlying probability distribution. Using this test we obtained qualitatively similar results. To validate these results, we compared orientation tuning functions of neurons that were tracked across days. Consistent with the claim that the same neurons were recorded across days and the fact that the monkey was not performing a learning task, the distribution of tuning differences of stable and orientation-tuned neurons across days was highly significantly different (Wilcoxon rank sum test, n1 = 79, n2 = 582, p < 10^-34) from the distribution of tuning differences across different neurons. Our results show that using only waveform information it is possible to reliably track stable neurons across days with a limited type I error probability. This statistical approach is particularly important since, in a learning experiment, properties of neurons such as orientation tuning are potentially changed and therefore cannot be used to evaluate stability. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.sfn.org/am2007/ Biologische Kybernetik Max-Planck-Gesellschaft San Diego, CA, USA 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007) en aeckerASEcker AGSiapas hoenselaarAHoenselaar berensPBerens georgeGAKeliris nikosNKLogothetis atoliasASTolias poster 4731 Studying the effects of noise correlations on population coding using a sampling method 2007 9 21-22 Responses of single neurons to a fixed stimulus are usually both variable and highly ambiguous. Therefore, it is widely assumed that stimulus parameters are encoded by populations of neurons. An important aspect in population coding that has received much interest in the past is the effect of correlated noise on the accuracy of the neural code. Theoretical studies have investigated the effects of different correlation structures on the amount of information that can be encoded by a population of neurons based on Fisher Information. Unfortunately, to be analytically tractable, these studies usually have to make certain simplifying assumptions such as high firing rates and Gaussian noise. Therefore, it remains open if these results also hold in the more realistic scenario of low firing rates and discrete, Poisson-distributed spike counts. In order to address this question we have developed a straightforward and efficient method to draw samples from a multivariate near-maximum entropy Poisson distribution with arbitrary mean and covariance matrix based on the dichotomized Gaussian distribution [1]. The ability to extensively sample data from this class of distributions enables us to study the effects of different types of correlation structures and tuning functions on the information encoded by populations of neurons under more realistic assumptions than analytically tractable methods. Specifically, we studied how limited range correlations (neurons with similar tuning functions and low spatial distance are more correlated than others) affect the accuracy of a downstream decoder compared to uniform correlations (correlations between neurons are independent of their properties and locations). Using a set of neurons with equally spaced orientation tuning functions, we computed the error of an optimal linear estimator (OLE) reconstructing stimulus orientation from the neurons firing rates. We findsupporting previous theoretical resultsthat irrespective of tuning width and the number of neurons in the network, limited range correlations decrease decoding accuracy while uniform correlations facilitate accurate decoding. The optimal tuning width, however, did not change as a function of either the correlation structure or the number of neurons in the network. These results are particularly interesting since a number of experimental studies report limited range correlation structures (starting at around 0.1 to 0.2 for similar neurons) while experiments carried out in our own lab suggest that correlations are generally low (on the order of 0.01) and uniform. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Schölkopf Department Logothetis Research Group Bethge http://www.gatsby.ucl.ac.uk/nccd/nccd07/abstract_book.pdf Biologische Kybernetik Max-Planck-Gesellschaft Hossegor, France Neural Coding, Computation and Dynamics (NCCD 07) en aeckerASEcker berensPBerens mbethgeMBethge nikosNKLogothetis atoliasASTolias poster 4272 A Data Management System for Electrophysiological Data Analysis Neuroforum 2007 4 13 Supplement 1222 Recent advances in both electrophysiological recording techniques and hardware capabilities have enabled researchers to simultaneously record from a large number of neurons in different areas of the brain. This opens the door for a wide range of complex analyses potentially leading to a better understanding of the principles underlying neural network computations. At the same time, due to the increasing amount of data with increasing complexity, significantly more emphasis has to be put on the data analysis task. Although high-level scripting languages such as Matlab can speed up the development of analysis tools, in our experience, a too large amount of time is still spent on (re)structuring and (re)organizing data for specific analyses. Therefore, our goal was to develop a system which enables experimental neuroscientists to spend less time on organizing their data and more on data collection and creative analysis. We developed an object oriented Matlab toolbox which supplies the user with basic data types and functions to organize and structure various types of electrophysiological data. By using an object oriented, hierarchical layout, basic functionality, such as integration of metadata, or storage and retrieval of data and results, is implemented independent of specific data formats or experimental designs. This provides maximal flexibility and compatibility with future experiments and new data formats. All data and experimental results are stored in a database, so the experimenter can choose which data to keep in memory for faster access and which to save to disk to save resources. Additionally, we have created an extensive library of basic analysis and visualization tools that can be used to get an overview of the data. http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/EckerTolias_2007_ADataManagement_4272[0].pdf http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://nwg.glia.mdc-berlin.de/media/pdf/conference/Proceedings-Goettingen2007.pdf Biologische Kybernetik Max-Planck-Gesellschaft Göttingen, Germany 7th Meeting of the German Neuroscience Society, 31st Göttingen Neurobiology Conference en aeckerASEcker berensPBerens georgeGAKeliris nikosNKLogothetis atoliasASTolias poster 4273 Orientation tuning of the local field potential and multi-unit activity in the primary visual cortex of the macaque Neuroforum 2007 4 13 Supplement 735 Oscillations in the local field potential (LFP) are abundant across species and brain regions. The possible relationship of these low-frequency extracelluar voltage fluctuations with the activity of the underlying local population of neurons remains largely elusive. To study this relationship, we used an array of chronically implanted tetrodes spanning a distance of 700 &#956;m and simultaneously recorded action potentials from multiple well-isolated single units, multi unit activity (MUA) and LFP from area V1 of the awake, behaving macaque. Moving and static gratings of different orientations were used for visual stimulation. In agreement with previous studies we find that the increase of LFP gamma-band power is a function of the orientation of the stimulus. However, the power of the gamma-band contains much less information about the orientation of the stimulus than the MUA and SUA recorded at the same site (Figure 1A). The average discriminability d&lsquo; between preferred and orthogonal orientation was 2.46 for MUA, 2.45 for SUA and 1.01 for the LFP. Moreover, in contrast to recent results from area MT (Liu and Newsome, 2006) we find only a weak correlation between the preferred orientation of the MUA tuning function and that of the LFP (Figure 1B, different colors indicate different animals). Interestingly, all nearby LFP recording sites in our array were tuned to a similar orientation while the preferred orientations of MUA tuning functions were widely scattered. These results suggest that the power of LFP signals does not capture local population activity at the scale of orientation columns in area V1. http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/T16-4C_[0].pdf http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://nwg.glia.mdc-berlin.de/media/pdf/conference/Proceedings-Goettingen2007.pdf Biologische Kybernetik Max-Planck-Gesellschaft Göttingen, Germany 7th Meeting of the German Neuroscience Society, 31st Göttingen Neurobiology Conference en berensPBerens georgeGAKeliris aeckerASEcker nikosNKLogothetis atoliasASTolias poster 3949 Spikes are phase locked to the gamma-band of the local field potential oscillations in the primary visual cortex of the macaque 2006 6 39 Oscillations in the local field potential (LFP) are abundant across species and brain regions. The possible role of these oscillations in information processing in the primary visual cortex (V1) of the macaque still remains largely elusive despite that V1 is one of the most extensively studied brain areas. To this end, we used chronically implanted, multiple tetrodes and recorded the spiking activity of single neurons and LFPs from area V1 of the awake, behaving macaque. Moving and static gratings of different orientations were used for visual stimulation. In agreement with previous reports we find that the increase of the LFP gamma-band power is a function of the orientation of the stimulus. Surprisingly though, there is only a weak correlation between the peak of the multi-unit spiking activity orientation tuning functions and the peak of the orientation tuning function of the gamma-band power of the LFP. There is however a different kind of relationship between spikes and LFP. Namely, the timing of the spikes is not randomly distributed in time but instead is locked to the phase of the gamma-band of the LFP. Specifically, the spikes of 60 out of 151 well-isolated single units showed significant phase locking to the LFP (P<0.05, circular Rayleigh test). On average, the spikes occurred on the downward slope of the LFP oscillation. In contrast to the presence of phase precession reported in the rat hippocampus, the phase tuning in V1 is stable over time. Specifically, the preferred phase of the spikes does not seem to change over time during the presentation of the stimulus. Moreover, the preferred phase is not significantly modulated as a function of the orientation of the stimulus (Figure A). This temporal structuring of the spiking activity of neurons in V1 could allow coding of information in the temporal regime (Panzeri & Schultz, 2001). In addition it could also potentially synchronize populations of neurons (Fries 2005). We are currently investigating these conjectures. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.areadne.org/2006/ Biologische Kybernetik Max-Planck-Gesellschaft Santorini, Greece AREADNE 2006: Research in Encoding and Decoding of Neural Ensembles en berensPBerens aeckerASEcker hoenselaarAHoenselaar georgeGAKeliris AGSiapas nikosNKLogothetis atoliasASTolias poster ToliasEKSSL2006 Structure of interneuronal correlations in the primary visual cortex of the rhesus macaque 2006 3 13 Despite recent progress in systems neuroscience, basic properties of the neural code still remain obscure. For instance, the responses of single neurons are both highly variable and ambiguous (similar responses can be elicited by different types of stimuli). This variability/ambiguity has to be resolved by considering the joint pattern of firing of multiple single units responding simultaneously to a stimulus. Therefore, in order to understand the underlying principles of the neural code it is important to characterize the correlations between neurons and the impact that these correlations have on the amount of information that can be encoded by populations of neurons. Here we applied the technique of chronically implanted, multiple tetrodes to record simultaneously from a number of neurons in the primary visual cortex (V1) of the awake behaving macaque, and to measure the correlations in the trial-to-trial fluctuations of their firing rates under the same stimulation conditions (noise correlations). We find that, contrary to widespread belief, noise correlations in V1 are very small (around 0.01) and do not change systematically neither as a function of cortical distance (up to 600 um) nor as a function of the similarity in stimulus preference between the neurons (uniform correlation structure). Interestingly, a uniform correlation structure is predicted by theory to increase the achievable encoding accuracy of a neuronal population and may reflect a universal principle for population coding throughout the cortex. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis http://www.cosyne.org/c/index.php?title=Cosyne_06 Salt Lake City, UT, USA Computational and Systems Neuroscience Meeting (COSYNE 2006) atoliasASTolias aeckerAEcker georgeGAKeliris TGSiapas ssmirnakisSMSmirnakis nikosNKLogothetis thesis 5104 Predictive Coding and Spike Timing Dependent Plasticity in Primary Visual Cortex 2008 3 http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/diplthesis_ae_final_[0].pdf http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Biologische Kybernetik Max-Planck-Gesellschaft Universität Tübingen, Tübingen, Germany Diplom en aeckerASEcker conference WallisFEGWB2016 Towards matching the peripheral visual appearance of arbitrary scenes using deep convolutional neural networks Perception 2016 8 30 45 ECVP Abstract Supplement 175-176 Distortions of image structure can go unnoticed in the visual periphery, and objects can be harder to identify (crowding). Is it possible to create equivalence classes of images that discard and distort image structure but appear the same as the original images? Here we use deep convolutional neural networks (CNNs) to study peripheral representations that are texture-like, in that summary statistics within some pooling region are preserved but local position is lost. Building on our previous work generating textures by matching CNN responses, we first show that while CNN textures are difficult to discriminate from many natural textures, they fail to match the appearance of scenes at a range of eccentricities and sizes. Because texturising scenes discards long range correlations over too large an area, we next generate images that match CNN features within overlapping pooling regions (see also Freeman and Simoncelli, 2011). These images are more difficult to discriminate from the original scenes, indicating that constraining features by their neighbouring pooling regions provides greater perceptual fidelity. Our ultimate goal is to determine the minimal set of deep CNN features that produce metameric stimuli by varying the feature complexity and pooling regions used to represent the image. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge Abstract Talk http://journals.sagepub.com/doi/full/10.1177/0301006616671273 Barcelona, Spain 39th European Conference on Visual Perception (ECVP 2016) 10.1177/0301006616671273 TSWallis CMFunke aeckerASEcker LAGatys felixFAWichmann mbethgeMBethge conference Ecker2016 What's the Signal in the Noise? 2016 6 28 Responses of cortical neurons are highly variable. Even repeated presentations of the same visual stimulus never elicit the same spike train. Identifying the origins of this variability remains a challenge. There is increasing evidence that it is not just noise arising from stochastic features of neuronal architecture, but at least partly represents meaningful top-down signals. One of the most prominent examples of such top-down modulation in the visual system is covert attention. I will present both theoretical and experimental results showing that trial-totrial fluctuations of attentional state contribute significantly to response variability in primary visual cortex of awake, behaving monkeys. I will argue that much can be learned about information processing in the brain by using latent variable models of neuronal activity to help us identify and account for cognitive variables and make sense of single-trial neural population data. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Invited Lecture http://areadne.org/2016/pezaris-hatsopoulos-2016-areadne.pdf Santorini, Greece AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles aeckerASEcker conference DenfieldET2015 Correlated variability in population activity: noise or signature of internal computations? 2015 10 19 45 372.05 Neuronal responses to repeated presentations of identical visual stimuli are variable. The source of this variability is unknown, but it is commonly treated as noise and seen as an obstacle to understanding neuronal activity. We argue that this variability is not noise but reflects, and is due to, computations internal to the brain. Internal signals such as cortical state or attention interact with sensory information processing in early sensory areas. However, little research has examined the effect of fluctuations in these signals on neuronal responses, leaving a number of uncontrolled parameters that may contribute to neuronal variability. One such variable is attention, which increases neuronal response gain in a spatial and feature selective manner. Both the strength of this modulation and the focus of attention are likely to vary from trial to trial, and we hypothesize that these fluctuations are a major source of neuronal response variability and covariability. We first examine a simple model of a gain-modulating signal acting on a population of neurons and show that fluctuations in attention can increase individual and shared variability and generate a variety of correlation structures that are relevant to population coding, including limited range and differential correlations. To test our model’s predictions experimentally, we devised a cued-spatial attention, change-detection task to induce varying degrees of fluctuation in the subject’s attentional signal by changing whether the subject must attend to one stimulus location while ignoring another, or attempt to attend to multiple locations simultaneously. We use multi-electrode recordings with laminar probes in primary visual cortex of macaques performing this task. We demonstrate that attention gain-modulates responses of V1 neurons in a manner that is consistent with results from higher-order areas. Consistent with our model’s predictions, our preliminary results indicate neuronal covariability is elevated in conditions in which attention fluctuates and that neurons are nearly independent when attention is focused. Overall, our results suggest that attentional fluctuations are an important contributor to neuronal variability and open the door to the use of statistical methods for inferring the state of these signals on a trial-by-trial basis. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Abstract Talk http://www.sfn.org/am2015/ Chicago, IL, USA 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015) GDenfield aeckerAEcker atoliasATolias conference GatysETB2015 Synaptic unreliability facilitates information transmission in balanced cortical populations 2015 3 16 Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examined how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we found that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. This finding provides a parsimonious explanation why cortex can afford to operate with noisy synapses. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge Abstract Talk http://www.dpg-verhandlungen.de/year/2015/conference/berlin/part/bp/session/8/contribution/5 Berlin, Germany 79. Jahrestagung der Deutschen Physikalischen Gesellschaft und DPG-Frühjahrstagung LAGatys aeckerASEcker TTchumatchenko mbethgeMBethge conference DenfieldET2015_2 The Role of Internal Signals in Structuring V1 Population Activity 2015 2 19 Neuronal responses to repeated presentations of identical visual stimuli are variable. The cause of this variability is unknown, but it is commonly treated as noise and seen as an obstacle to understanding neuronal activity. We offer an alternative explanation: this variability is not noise but reflects, and is due to, computations internal to the brain. Internal signals such as cortical state or attention interact with sensory information processing in early sensory areas. However, little research has examined the effect of fluctuations in these signals on neuronal responses, leaving a number of uncontrolled parameters that may contribute to neuronal variability. One such variable is attention. We hypothesize that fluctuations in attentional signals contribute to neuronal response variability and that controlling for such fluctuations will reduce this variability. To study this interaction, we use multi-electrode recordings with laminar probes in primary visual cortex of macaques while subjects perform a cued-spatial attention, change-detection task. We induce varying degrees of fluctuation in the subject’s attentional signal by changing whether the subject must attend to one stimulus location while ignoring another, or attempt to attend to both locations simultaneously. We demonstrate that attention increases stimulusevoked firing rates and gain-modulates the tuning curves of V1 neurons in a manner that is consistent with results from higher order areas. Future experiments will examine the effect of attentional fluctuations on neuronal response variability and interneuronal correlations as well as the laminar profile of these effects. Under this hypothesis, this variability can aid, rather than hinder, our understanding of brain function. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Abstract Talk https://media.bcm.edu/documents/2015/93/neuroscienceabstractbook2015.pdf Houston, TX, USA 25th Annual Rush and Helen Record Neuroscience Forum GHDenfield aeckerAEcker atoliasATolias conference GatysETB2014_2 Synaptic unreliability facilitates information transmission in balanced cortical populations 2014 10 13 15 11 Cortical neurons fire in a highly irregular manner, suggesting that their input is tightly balanced and changes in presynaptic firing rate are encoded primarily in the variance of the postsynaptic currents. Here we show that such balance has a surprising effect on information transmission: Synaptic unreliability which is ubiquitous in cortex and usually thought to impair neural communication actually increases the information rate. We show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not rely on a threshold nonlinearity. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Abstract Talk http://www.neuroschool-tuebingen-nena.de/fileadmin/user_upload/Dokumente/neuroscience/Abstractbook_NeNa2014_final.pdf Schramberg, Germany 15th Conference of Junior Neuroscientists of Tübingen (NeNa 2014) LGatys aeckerAEcker TTchumatchenko mbethgeMBethge conference GatysETB2014 Synaptic unreliability facilitates information transmission in balanced cortical populations 2014 9 4 21 Cortical neurons fire in a highly irregular manner, suggesting that their input is tightly balanced and changes in presynaptic firing rate are encoded primarily in the variance of the postsynaptic currents. Here we show that such balance has a surprising effect on information transmission: Synaptic unreliability – which is ubiquitous in cortex and usually thought to impair neural communication – actually increases the information rate. We show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not rely on a threshold nonlinearity. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge Abstract Talk http://abstracts.g-node.org/abstracts/65d2bbbf-5b2d-4570-8200-f994f190e9ca Göttingen, Germany Bernstein Conference 2014 10.12751/nncn.bc2014.0017 LAGatys aeckerASEcker TTchumatchenko mbethgeMBethge conference Ecker2014 State dependence of noise correlations in primary visual cortex 2014 3 21 http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Invited Lecture http://www.ewcbr.eu/index.php/en/abstracts-112.html Brides-les-Bains, France 34th European Winter Conference on Brain Research and European Brain and Behaviour Society (EWCBR/EBBS 2014) aeckerAEcker conference Ecker2013 State dependence of noise correlations in macaque primary visual cortex 2013 7 18 The structure and magnitude of noise correlations in the monkey visual system has been subject to intense debate over the last couple of years. We previously found that neural responses to repeated presentations of the same visual stimulus were close to independent in V1 of awake, fixating monkeys (average rsc: 0.01). Other labs, in contrast, found average levels of correlations up to an order of magnitude higher. Although a number of possible explanations for this discrepancy have been put forward, only few of them have been directly addressed. We tested one of our original hypotheses, that fluctuations of global brain state under anesthesia may induce positive correlations between neurons, which are absent during wakefulness. We performed multi-tetrode recordings in V1 of opiod-anesthetized monkeys under conditions otherwise identical to our previous awake recordings. Activity in anesthetized monkey V1 was dominated by strong coordinated fluctuations involving nearly every active neuron. These state fluctuations evolve on a timescale of 1–2 Hz, substantially slower than what would be expected from shared sensory noise, and resemble up and down states, which have been described for many other types of anesthetics before. During wakefulness, in contrast, such state fluctuations were absent. We further found that after accounting for the brain state under anesthesia the level of noise correlations was reduced to that during wakefulness. Our results highlight an important caveat of neural population recordings under anesthesia: if not properly accounted for, state fluctuations, which are not present in awake animals, are the primary source of correlated variability. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Invited Lecture http://www.gnt.ens.fr/~sostojic/cns2013_workshop.html Paris, France CNS 2013 Workshop on Functional Role of Correlations: Theory and Experiment aeckerAEcker conference BerensGEB2009 Neurometric function analysis of short-term population codes Frontiers in Computational Neuroscience 2009 10 1 2009 Conference Abstract: Bernstein Conference on Computational Neuroscience 24-25 The relative merits of different population coding schemes have mostly been studied in the framework of stimulus reconstruction using Fisher Information, minimum mean square error or mutual information. Here, we analyze neural population codes using the minimal discrimination error (MDE) and the Jensen-Shannon information in a two alternatives forced choice (2AFC) task. In a certain sense, this approach is more informative than the previous ones as it defines an error that is specific to any pair of possible stimuli - in particular, it includes Fisher Information as a special case. We demonstrate several advantages of the minimal discrimination error: (1) it is very intuitive and easier to compare to experimental data, (2) it is easier to compute than mutual information or minimum mean square error, (3) it allows studying assumption about prior distributions, and (4) it provides a more reliable assessment of coding accuracy than Fisher information. First, we introduce the Jensen-Shannon information and explain how it can be used to bound the MDE. In particular, we derive a new lower bound on the minimal discrimination error that is tighter than previous ones. Also, we explain how Fisher information can be derived from the Jensen-Shannon information and conversely to what extent Fisher information can be used to predict the minimal discrimination error for arbitrary pairs of stimuli depending on the properties of the tuning functions. Second, we use the minimal discrimination error to study population codes of angular variables. In particular, we assess the impact of different noise correlations structures on coding accuracy in long versus short decoding time windows. That is, for long time window we use the common Gaussian noise approximation while we analyze the Ising model with identical noise correlation structure to address the case of short time windows. As an important result, we find that the beneficial effect of stimulus dependent correlations in the absence of 'limited-range' correlations holds only true for long-term population codes while they provide no advantage in case of short decoding time windows. In this way, we provide for a new rigorous framework for assessing the functional consequences of correlation structures for the representational accuracy of neural population codes in short time scales. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Research Group Bethge Department Logothetis Department Schölkopf Abstract Talk http://www.frontiersin.org/10.3389/conf.neuro.10.2009.14.093/event_abstract Frankfurt a.M., Germany Bernstein Conference on Computational Neuroscience (BCCN 2009) 10.3389/conf.neuro.10.2009.14.093 berensPBerens sgerwinnSGerwinn aeckerASEcker mbethgeMBethge conference ToliasEKPPL2007 Population codes, correlations and coding uncertainty 2007 9 16 Despite progress in systems neuroscience the neural code still remains elusive. For instance, the responses of single neurons are both highly variable and ambiguous (similar responses can be elicited by different types of stimuli). This variability/ambiguity has to be resolved by considering the joint pattern of firing of multiple single units responding simultaneously to a stimulus. Therefore, in order to understand the underlying principles of the neural code it is imperative to characterize the correlations between neurons and the impact that these correlations have on the amount of information encoded by populations of neurons. We use chronically implanted tetrode arrays to record simultaneously from many neurons in the primary visual cortex (V1) of awake, behaving macaques. We find that the correlations in the trial-to-trial fluctuations of their firing rates between neurons under the same stimulation conditions (noise correlations) in V1 were very small (around 0.01 in 500 ms bin window) during passive viewing of sinusoidal grating stimuli. We are also measuring correlations in extrastriate visual areas and investigating the impact of correlations on encoding stimulus uncertainty by neuronal populations, under different stimulus and behavioral conditions. http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Research Group Bethge Abstract Talk http://www.gatsby.ucl.ac.uk/nccd/nccd07/abstract_book.pdf Hossegor, France Neural Coding, Computation and Dynamics (NCCD 07) atoliasASTolias aeckerAEcker georgeGAKeliris theofanisFPanagiotaropolulos stefanoSPanzeri nikosNKLogothetis conference 3723 Structure of interneuronal correlations in the primary visual cortex of the Rhesus macaque 2005 11 35 591.12 Despite recent progress in systems neuroscience, basic properties of the neural code still remain obscure. For instance, the responses of single neurons are both highly variable and ambiguous (similar responses can be elicited by different types of stimuli). This variability/ambiguity has to be resolved by considering the joint pattern of firing of multiple single units responding simultaneously to a stimulus. Therefore, in order to understand the underlying principles of the neural code it is important to characterize the correlations between neurons and the impact that these correlations have on the amount of information that can be encoded by populations of neurons. Here we applied the technique of chronically implanted, multiple tetrodes to record simultaneously from a number of neurons in the primary visual cortex (V1) of the awake behaving macaque, and to measure the correlations in the trial-to-trial fluctuations of their firing rates under the same stimulation conditions (noise correlations). We find that, contrary to widespread belief, noise correlations in V1 are very small (around 0.01) and do not change systematically neither as a function of cortical distance (up to 600 m) nor as a function of the similarity in stimulus preference between the neurons (uniform correlation structure). Interestingly, a uniform correlation structure is predicted by theory to increase the achievable encoding accuracy of a neuronal population and may reflect a universal principle for population coding throughout the cortex. Support Contributed By: MPI, NEI(NIH) http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de http://www.kyb.tuebingen.mpg.de Department Logothetis Abstract Talk http://www.sfn.org/absarchive/ Biologische Kybernetik Max-Planck-Gesellschaft Washington, DC, USA 35th Annual Meeting of the Society for Neuroscience (Neuroscience 2005) en atoliasASTolias georgeGAKeliris aeckerASEcker AGSiapas ssmirnakisSMSmirnakis nikosNKLogothetis