@Article{ ErokhinBGCPRRSS2012,
title = {Stochastic hybrid 3D matrix: learning and adaptation of electrical properties},
journal = {Journal of Materials Chemistry},
year = {2012},
month = {9},
volume = {22},
number = {43},
pages = {22881-22887},
abstract = {Memristive devices are electronic elements with memory properties. This feature marks them out as possible candidates for mimicking synapse properties. Development of systems capable of performing simple brain operations demands a high level of integration of elements and their 3D organization into networks. Here, we demonstrate the formation and electrical properties of stochastic polymeric matrices. Several features of the network revealed similarities with those of the nervous system. In particular, applying different training protocols, we obtained two kinds of learning comparable to the “baby” and “adult” learning in animals and humans. To mimic “adult” learning, multi-task training was applied simultaneously resulting in the formation of few parallel pathways for a given task, modifiable by successive training. To mimic “baby” learning (imprinting), single task training was applied at one time, resulting in the formation of multiple parallel signal pathways, scarcely influenced by successive training.},
web_url = {http://pubs.rsc.org/en/content/articlepdf/2012/jm/c2jm35064e},
state = {published},
DOI = {10.1039/C2JM35064E},
author = {Erokhin V, Berzina T, Gorshkov K, Camorani P, Pucci A, Ricci L, Ruggeri G, Sigala R{sigala}{Department Physiology of Cognitive Processes} and Sch\"uz A{schuez}{Department Physiology of Cognitive Processes}}
}

@Article{ SigalaLR2011,
title = {Own-species bias in the representations of monkey and human face categories in the primate temporal lobe},
journal = {Journal of Neurophysiology},
year = {2011},
month = {6},
volume = {105},
number = {6},
pages = {2740-2752},
abstract = {Face categorization is fundamental for social interactions of primates and is crucial for determining conspecific groups and mate choice. Current evidence suggests that faces are processed by a set of well-defined brain areas. What is the fine structure of this representation, and how is it affected by visual experience? Here, we investigated the neural representations of human and monkey face categories using realistic three-dimensional morphed faces that spanned the continuum between the two species. We found an “own-species” bias in the categorical representation of human and monkey faces in the monkey inferior temporal cortex at the level of single neurons as well as in the population response analyzed using a pattern classifier. For monkey and human subjects, we also found consistent psychophysical evidence indicative of an own-species bias in face perception. For both behavioural and neural data, the species boundary was shifted away from the center of the morph continuum, for each species toward their own face category. This shift may reflect visual expertise for members of one's own species and be a signature of greater brain resources assigned to the processing of privileged categories. Such boundary shifts may thus serve as sensitive and robust indicators of encoding strength for categories of interest.},
web_url = {http://jn.physiology.org/content/105/6/2740.full.pdf+html},
state = {published},
DOI = {10.​1152/​jn.​00882.​2010},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Article{ 4315,
title = {Visual neuroscience: face-encoding mechanisms revealed by adaptation},
journal = {Current Biology},
year = {2007},
month = {1},
volume = {17},
number = {1},
pages = {R20-R22},
abstract = {Faces convey a great variety of information, for example about the species, gender, age, identity and even mood or intentions. A recent study sheds light on the neural mechanisms for encoding a face‘s gaze direction.},
web_url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6VRT-4MSB2XV-F-3&_cdi=6243&_user=29041&_orig=browse&_coverDate=01%2F09%2F2007&_sk=999829998&view=c&wchp=dGLbVzW-zSkzk&md5=2975d931feb9b32c411f7633350b35b7&ie=/sdarticle.pdf},
state = {published},
DOI = {10.1016/j.cub.2006.11.038},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Inproceedings{ SigalaSE2011,
title = {Adaptive Properties of Stochastic Memristor Networks: A Computational Study},
year = {2011},
month = {5},
pages = {312-313},
abstract = {A ‘memristor’ is a passive two-terminal circuit element the electric resistance of which depends on the history of the charge that has passed through it. We implemented a platform to simulate adaptive properties of stochastic memristor networks. We showed that such networks follow a stable behavior that diverges from its initial state depending on the history of stimulation. Additionally, we observed that the connectivity patterns of the networks influence their adaptive properties. These results confirm the adaptive properties of statistical memristor networks and suggest that they can be potentially used as complex and self-assembled ‘learning machines’.},
file_url = {fileadmin/user_upload/files/publications/2011/FET-2011-Sigala.pdf},
web_url = {http://www.fet11.eu/},
publisher = {Elsevier},
address = {Amsterdam, Netherlands},
booktitle = {Procedia Computer Science Volume 7},
event_name = {2nd European Future Technologies Conference and Exhibition (FET 11)},
event_place = {Budapest, Hungary},
state = {published},
DOI = {10.1016/j.procs.2011.09.021},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Smerieri A and Erokhin V}
}

@Inproceedings{ 5537,
title = {Learning features of intermediate complexity for the recognition of biological motion},
journal = {ICANN 2005. Lecture notes in computer science   ISSN 0302-9743},
year = {2005},
month = {9},
pages = {241-246},
abstract = {Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have not yet been clarified, one possible explanation is that the visual system extracts specific motion features that are suitable for the robust recognition of both normal and degraded stimuli. We present a neural model for biological motion recognition that learns robust mid-level motion features in an unsupervised way using a neurally plausible memory-trace learning rule. Optimal mid-level features were learnt from image motion sequences containing a walker with, or without background motion clutter. After learning of the motion features, the detection performance of the model substantially increases, in particular in presence of clutter. The learned mid-level motion features are characterized by horizontal opponent motion, where this feature type arises more frequently for the training stimuli without motion clutter. The learned features are consistent with recent psychophysical data that indicates that opponent motion might be critical for the detection of point light walkers.},
file_url = {/fileadmin/user_upload/files/publications/ICANNp2005_[0].pdf},
web_url = {http://www.ibspan.waw.pl/ICANN-2005/},
editor = {Duch, W. , J. Kacprzyk, E. Oja, S. Zadrozny},
publisher = {Springer},
address = {Berlin, Germany},
booktitle = {Artificial Neural Networks: Biological Inspirations – ICANN 2005},
event_name = {15th International Conference on Artificial Neural Networks},
event_place = {Warsaw, Poland},
state = {published},
ISBN = {978-3-540-28752-0},
DOI = {10.1007/11550822_39},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Serre T, Poggio T and Giese M{giese}}
}

@Inproceedings{ 3287,
title = {NSL/ASL: Simulation of Neural based Visuomotor Systems},
journal = {Proceedings of the International Joint Conference on Neural Networks (IJCNN‘01)},
year = {2001},
month = {7},
pages = {1065-1070},
abstract = {Through experimentation and simulation scientists are
able to get an understanding of the underlying biological
mechanisms involved in living organisms. These
mechanisms, both structural and behavioral, serve as
inspiration in the modeling of neural based architectures
as well as in the implementation of robotic systems.
Among these, we are particularly motivated in studying
animals such as toads, frogs, salamanders and praying
mantis that rely on visuomotor coordination. In order to
deal with the underlying complexity of these systems, we
have developed the NSLIASL simulation system to enable
modeling and simulation at different levels of granularity.},
web_url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00939508},
publisher = {IEEE},
address = {Piscataway, NJ, USA},
event_name = {International Joint Conference on Neural Networks (IJCNN'01)},
event_place = {Washington, DC, USA},
state = {published},
ISBN = {0-7803-7044-9},
DOI = {10.1109/IJCNN.2001.939508},
author = {Weitzenfeld A, Cervantes F and Sigala R{sigala}}
}

@Poster{ 7068,
title = {"Own-species" bias in the categorical representation of a human/monkey continuum in the human and non-human primate temporal lobe},
year = {2010},
month = {11},
volume = {40},
number = {581.20},
abstract = {While face categorization is a fundamental cognitive ability of human and non-human primates, its neural basis remain poorly understood. Using a new morphing technique, we created realistic three-dimensional morphed faces that linearly span the continuum between humans and monkeys (“species” continuum). Extensive categorization and discrimination experiments in human observers show that humans perceive the “species” continuum categorically. Moreover, the position of the categorical boundary is shifted from the center towards the human end of the continuum, suggesting a higher sensitivity to changes near the own-species prototype. We presented a subset of these faces to human subjects in a block-design fMRI experiment to record BOLD signals from the temporal lobe while participants performed an unrelated task at fixation. We applied a multivariate approach based on (Pearson) correlations to compute the difference between activity patterns elicited by faces along the continuum. Using this method, we looked for a categorical representation in face selective areas previously defined using an independent, standard "Face-localizer" experiment. Consistent with the psychophysical results, we found a categorical response with a bias towards the human end of the stimulus continuum in the activation patterns of the left human STS. In addition, activation in human ventral temporal cortex was most sensitive to deviations from the human prototype. To look for similar effects in monkeys, we applied an equivalent multivariate approach to analyze extracellular signals from a population of neurons recorded from the STS of two macaque monkeys while they fixated at the same type of faces. Additionally, the position of the perceptual category boundary was determined with a preferential-looking-time experiment. In both behavioral and neuronal monkey data, we found a categorical representation of the continuum, but in this case, with a bias towards the monkey end of the continuum. Our results demonstrate the neural basis of categorical representation of a facial attribute in the human and non-human primate brain. Together, our findings suggest that experience can lead to significant shifts in category boundary for face stimuli.},
web_url = {http://www.sfn.org/am2010/index.aspx?pagename=abstracts_main},
event_name = {40th Annual Meeting of the Society for Neuroscience (Neuroscience 2010)},
event_place = {San Diego, CA, USA},
state = {published},
author = {Sigala Alanis GR{sigala}{Department Physiology of Cognitive Processes}, Schultz J{johannes}{Department Human Perception, Cognition and Action}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 6818,
title = {Categorical Representation of a Human/Monkey Face Continum in the Human and Non-Human Primate Temporal Lobe},
year = {2010},
month = {6},
volume = {2010},
pages = {93},
abstract = {Categorization of faces is fundamental for social interactions of primates. To understand its neural basis, we investigate how human and monkey face categories are represented in both the human and non-human primate brain. As stimuli, we use realistic three-dimensional morphed faces that linearly span the continuum between humans and monkeys (Fig. 1A).
Extensive behavioral tests in both species revealed categorical perception with a shift of the categorical boundary towards the own species (Fig. 1B). This suggests that both species perceive the same stimulus continuum in a fundamentally different way. During a fixation task, we recorded from the temporal lobe extracellular signals in monkeys and BOLD signals in humans. To analyze the data, we used a multivariate pattern classifier approach based on
Support Vector Machines and correlations. Consistent with the psychophysical results, we found an "own-species" bias in the categorical representation of human and monkey faces
at the level of single neurons as well as in the population response in the inferior temporal lobe of the monkey. (Fig. 1C). Symmetrically, we found a categorical response with an ownspecies bias in the activation patterns of the left human STS. In addition, human ventral temporal cortex showed a higher sensitivity for human faces. Our results are the first to demonstrate the neural basis of categorical representation of a facial attribute in the
primate brain. In addition, our data show that both psychophysical and neuronal data can show categorical boundary shifts indicative of the behavioral relevance of prototypical categories.},
web_url = {http://www.areadne.org/2010/home.html},
editor = {Hatsopoulos, N. G., S. Pezaris},
event_name = {AREADNE 2010: Research in Encoding And Decoding of Neural Ensembles},
event_place = {Santorini, Greece},
state = {published},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Schultz J{johannes}{Department Human Perception, Cognition and Action}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 6283,
title = {Category selectivity in features of the local field potentials and single cell activity simultaneously recorded from the inferior temporal cortex of the macaque monkey},
year = {2009},
month = {10},
volume = {39},
number = {262.6},
abstract = {Although there is evidence suggesting that the inferior temporal (IT) cortex plays an important role in face processing and categorization, the exact neural mechanisms underlying these cognitive functions remain unknown. Here we address this issue by simultaneously recording the local field potentials (LFP) and single cells activity at 202 sites of the inferior temporal cortex (IT) of two macaques, while they passively fixated at pictures of monkey faces, human faces and objects. Our first goal was to investigate which features of the LFP, in frequency and time domains, were able to represent natural categories. For that, we calculated a selectivity index at two granularity levels: face vs. object (‘coarse’ selectivity) and monkey vs. human faces (‘fine’ selectivity). Our second goal was to study correlations between the selectivity of the LFP features and the selectivity of single cells recorded at the same sites. The data was first pre-processed as follows: for the LFPs we computed on each recording site: a) Visual-evoked-potentials (VEPs) and b) Single-trial-based instantaneous power and phase for different frequency bands. For the single cells we calculated i) Mean firing rate across trials and ii) Mutual information between stimulus classes and their associated responses (on each single-trial). Regarding selectivity of the VEPs, specifically the P100 deflection, we found that its onset latency occurred earlier for faces than for objects (p<0.01) and for monkey than for human faces (p<0.05). In contrast, the P100 amplitude did not systematically differentiate between these categories. In the frequency domain, we found that the degree of phase-locking (across trials in single electrodes) of the theta-band (4-8 Hz) around the P100 (80 ms to 120 ms after stimulus presentation) discriminated between faces/objects (p<0.01) and humans/monkeys (p<0.01). Considering correlations between selectivity of the LFP features and single cells, we found that ‘coarse’ (faces vs. objects) selectivity of the VEPs, particularly when using the amplitude of the N170 deflection, and also selectivity of the phase-locking of the gamma-band (low gamma: 28-48 Hz) around P100, significantly correlated (p<0.05) with the information about faces and objects of the single cells at those locations. More effects on correlations between LFP features and single cell activity will be discussed during the presentation. By showing that time related features of neural signals can better discriminate “coarse” and “fine” differences, and describing relations between these features, we provide novel insights into the neural mechanisms of object and face recognition.},
web_url = {http://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=b7a30480-b3fd-481b-bc5c-1105adf9ca4e&cKey=88ead301-9a70-40b9-b39b-4030aba9566f},
event_name = {39th Annual Meeting of the Society for Neuroscience (Neuroscience 2009)},
event_place = {Chicago, IL, USA},
state = {published},
author = {Sigala Alanis GR{sigala}{Department Physiology of Cognitive Processes}, Veit J{jveit}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 6100,
title = {Encoding of object and face categories by simultaneously recorded local field potentials and single cell activity in the inferior temporal cortex of the macaque monkey},
journal = {Perception},
year = {2009},
month = {8},
volume = {38},
number = {ECVP Abstract Supplement},
pages = {78},
abstract = {We investigate to which extent signals recorded from the inferior temporal (IT) cortex of two macaque monkeys can discriminate between (i) faces vs objects, and (ii) monkey vs human faces. During a fixation task, we simultaneously recorded the local field potential (LFP) and spiking activity of single cells at 202 different sites. On each site we computed (i) visual-evoked potentials (VEP), (ii) single-trial-based instantaneous power and phase for different frequency bands, and (iii) spiking activity of single neurons. Considering the VEPs, specifically the P100 deflection, we found that its onset latency occurred earlier for faces than for objects ( p < 0.01) and for monkey than for human faces ( p < 0.05). In contrast, the P100 amplitude did not systematically differentiate between these categories. In the frequency domain, we found that the amount of phase-locking (across trials in single electrodes) of the theta-band around the P100 discriminated between faces/objects and humans/monkeys. Finally, we found that differences in the amount of phase-locking of the gamma-band around P100 between faces/objects were significantly correlated with faces/objects selectivity of single neurons at those locations. Our findings provide novel insights into the neural mechanisms of object and face recognition.},
web_url = {http://www.perceptionweb.com/abstract.cgi?id=v090792},
event_name = {32nd European Conference on Visual Perception},
event_place = {Regensburg, Germany},
state = {published},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Veit J{jveit}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 6099,
title = {Neural encoding of face-categories in the macaque temporal cortex},
year = {2009},
month = {4},
web_url = {http://ec.europa.eu/information_society/events/fet/2009/programme/poster_sessions/index_en.htm},
event_name = {European Future Technologies Conference (FET 2009)},
event_place = {Praha, Czech Republic},
state = {published},
author = {Sigala Alanis GR{sigala}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 6098,
title = {Timing of local field potential (LFP) responses in primate inferior temporal (IT) cortex distinguishes between monkey faces, human faces and objects},
year = {2008},
month = {11},
volume = {38},
number = {261.14},
abstract = {It is well established that the inferior temporal (IT) cortex of the macaque monkey contains cells that respond selectively to faces. How information about faces is represented and organized at the network level remains largely unknown. Here we simultaneously recorded local field potentials (LFPs) and spiking activity in the IT cortex of two monkeys fixating at realistic human, monkey faces and objects, to investigate the neural representation of these stimulus classes. Our previous results indicate that spike information recorded from single neurons clearly differentiates between these three classes of stimuli. Here we investigate whether LFPs also contain information about these three types of stimuli. From the visual evoked potentials (VEP), we reliably and automatically extracted (in 44/65 sites in monkey M1 and in 20/68 sites in monkey 20) different features that convey time or amplitude information about stimulus class. Specifically, we focused on the timing and amplitude of the so called “N70” (negative deflection after about 70 ms of stimulus presentation), “P100” (positive deflection at about 100 ms) and “N170” (positive deflection at about 170 ms) components of the VEP. We grouped the VEPs into three classes according to the stimulus: humans, monkeys and objects VEPs. We found in both monkeys that the onset time of the face VEPs was significantly faster compared to the object VEPs (ttests, P<0.01) for all three deflections. Moreover, the onset of these deflections was faster for the monkey face VEPs compared to the human face VEPs (ttests, P<0.05). By contrast, the amplitude of these deflections did not systematically vary between stimulus classes. These results suggest that timing information in the LFPs can be used to reliably discriminate between human and monkey face stimuli. Furthermore, activation evoked by monkey faces reaches IT cortex earlier than the one evoked by human faces. These findings suggest a privileged role for processing of own species faces in the macaque brain.},
web_url = {http://www.sfn.org/am2008/},
event_name = {38th Annual Meeting of the Society for Neuroscience (Neuroscience 2008)},
event_place = {Washington, DC, USA},
state = {published},
author = {Sigala Alanis GR{sigala}{Department Physiology of Cognitive Processes}, Veit J{jveit}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 5544,
title = {Decoding the perceptual boundary of human/monkey face categories from a population of neurons in the Inferior-Temporal (IT) cortex of the macaque monkey brain},
journal = {Frontiers in Neuroinformatics},
year = {2008},
month = {9},
number = {Conference Abstract: Neuroinformatics 2008},
abstract = {Faces have been intensively used in human and monkey subjects to study visual perception. However, due to the different approaches scientists have had to follow given the implicit differences in these two types of observers, there are few studies comparing face perception in both species, especially perceptual effects in humans and single cell recordings in behaving monkeys. Using a new computer vision algorithm based on Support Vector Machines (SVMs) we created realistic morphs by linearly interpolating three-dimensional information of human and monkey faces. We asked human observers to categorize these morphs as humans or monkeys and we found that they draw the category boundary closer to their own species (at approximately 60%human/40%monkey). We looked for the neural correlates of this effect recording the single-unit-activity (SUA) of neurons (194 in monkey M1 and 220 in monkey M2) in the IT cortex of two macaque monkeys while they fixate at those faces. Considering all recorded neurons, 85% in monkey M1 and 62% in monkey M2 were visually selective, 14% in monkey M1 and 4% in monkey M2 were face-selective and 8% in monkey M1 and 2% in monkey M2, were category-selective. To find out how these morphs are represented at the level of the population of all recorded neurons, we first reduced the dimensionality of the data applying the Principal Component Analysis (PCA) and using the best 10% of the principal components ranked according to the variance they explained. We used a pattern classifier (Support Vector Machine or SVM) to learn this new representation (form by the principal components) of the responses to human and monkey faces and classify the responses to ambiguous morphs into one of both categories. We found that in both cases (using the neural responses recorded in monkeys M1 and M2), and symmetric to the findings in humans, the classifier drew the category boundary closer to the monkey category (at approximately 40%human/60% monkey). These findings suggest an ‘own species’ advantage in the encoding of face stimuli by human and monkey observers. Our findings also indicate that this species-specific advantage is represented by a large fraction of neurons in the inferior temporal (IT) cortex of the monkey brain.},
web_url = {http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=2&pap=418&ind_abs=1&pg=1},
event_name = {1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain},
event_place = {Stockholm, Sweden},
state = {published},
DOI = {10.3389/conf.neuro.11.2008.01.102},
author = {Sigala RA{sigala}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 5549,
title = {Reading out the perceptual boundary between human and monkey face categories from the inferior temporal cortex of the macaque monkey},
journal = {Perception},
year = {2007},
month = {8},
volume = {36},
number = {ECVP Abstract Supplement},
pages = {218-219},
abstract = {We demonstrated that, when human subjects have to classify human/monkey morphed faces that change along a continuum, they draw the category boundary closer to their own species (at approximately 60% human/40% monkey). Considering that neurons in the infero-temporal (IT) cortex encode face information, we recorded the single-unit activity (SUA) of 118 neurons and the local field potential (LFP) at 58 sites of the IT cortex of one macaque monkey during fixation of morphed faces. Out of 118 single units, 85% were visually responsive and 23% were face cells according to standard criteria. We used a two-class (human - monkey) classifier (k-NearestNeighbor) to analyze the population activity of visually responsive units and all LFPs. Symmetric to the findings in humans, the classifier drew the category boundary closer to the monkey category (at approximately 40% human/60% monkey) for both kinds of neural signals. These results suggest an ‘own-species‘ advantage in the encod
ing of face stimuli. Our findings also indicate that a large fraction of IT neurons participate in the encoding of face categories.},
web_url = {http://www.perceptionweb.com/abstract.cgi?id=v070716},
event_name = {30th European Conference on Visual Perception},
event_place = {Arezzo, Italy},
state = {published},
author = {Alanis GRS{sigala}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 4886,
title = {Using the Local Field Potential (LFP) Recorded from the Inferior-Temporal Cortex of a Macaque Monkey to Study Species-Dependent (Monkey/human) Face Processing},
year = {2007},
month = {7},
volume = {10},
pages = {117},
abstract = {Recently, we have been able to read out a human/monkey face category-boundary from singleunit-
activity (SUA) recorded from the inferior-temporal (IT) cortex of a macaque monkey
brain. This data was collected in an experiment where monkeys have to fixate at pictures of
human/monkey morphed faces at different levels of this ‘species-continuum’. Consistent with
our previous psychophysical experiments in which human subjects have to categorize morphed
faces as humans or monkeys, the perceptual boundary seems to be shifted towards the
‘own-species’ category (approximately 60% human/40% monkey in humans and the other way
around in the monkey data). Similar to the ‘other-race’ effect, this effect suggests a perceptual
bias that could be due to long-term learning.
The local field potential (LFP) refers to the low-frequency (< 300Hz) component of signals
recorded from the brain, and it has been associated with dendritic activity within a particular
recording area. In this work we investigate to what extent these LFP signals are stimulus
selective and weather they correlate with our previous results obtained from the simultaneously
recorded spiking activity (SUA).To achieve that, we first extract different features from the LFP
signals such peak amplitude, time-onset or the spectral power of different frequency bands. To
evaluate the information content of these features in relation to our stimulus and the spiking
data, we use statistical analyses, information theory and pattern classification.
Preliminary results show that features such as peak onset-time and peak-amplitude differ
significantly across stimulus-conditions. In contrast to the spiking data, when using these features,
the pattern classifiers set the face category-border without a consistent shift towards the
monkey category. Further analysis of these features using information theory will be needed to
test possible correlations with the spiking data and the stimulus properties.},
web_url = {http://www.twk.tuebingen.mpg.de/twk07/abstract.php?_load_id=sigala01},
event_name = {10th Tübinger Wahrnehmungskonferenz (TWK 2007)},
event_place = {Tübingen, Germany},
state = {published},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Veit J{jveit}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 5545,
title = {Neural encoding of species dependent face-categories in the macaque temporal cortex},
journal = {Neuroforum},
year = {2007},
month = {4},
volume = {13},
number = {Supplement},
pages = {767},
abstract = {When perceiving a face, we can easily decide whether it belongs to a human or non-human primate. It is
thought that face information is represented by neurons in the macaque temporal cortex. However, the precise
encoding mechanisms used by these neurons remain unclear. Here we use face stimuli of humans, monkeys
and monkey-human hybrids (morphs) to gain a better understanding of these mechanisms, in particular of the
categorization of faces into different species, and how learning affects representation of these stimuli.
We perform single cell and local field potential (LFP) recordings in the inferior-temporal (IT) cortex of the
macaque brain during a fixation task. To investigate the perceptual effects of our stimuli and possible relations
to the neural data, we conduct in parallel psychophysical experiments with human subjects. On preliminary
results of 75 recorded cells in one animal, we found 66 visual responsive neurons. From them, 12 were tuned
to faces ('face-cells') and 9 to other test objects (like a hand, clock, fruits, etc.). Six 'face-cells' prefer monkeys
while just two prefer humans. Considering the population activity, monkey faces elicited in general higher
firing rates on the population of neurons (independent of its category) than human faces. Additionally, these
firing rates change gradually according to the human/monkey ratio of the morphed stimuli. After measuring
the perceptual category boundary between monkeys and humans faces in our human subjects, we founded that
it is shifted to the human side, independent of the method we use to measure it.
Our preliminary cell recordings suggest that neural responses (firing rates) of some cells differentiate between
monkey and human faces. Besides, the tuning curves of some neurons and the population correlate with the
human-ratio of the morphed stimuli. Our psychophysical experiments confirm, on the one hand, the perceptual
effect of our stimuli in which we manipulate the human-monkey ratio and, on the other hand showed a
tendency of our subjects to set the category boundary between humans and monkeys closer to the human side.
All these findings point to different mechanisms used by the brain to encode human and monkey faces, which
seem to be clearly represented by neurons in the inferior-temporal cortex of the money brain.},
web_url = {http://www.neuro.uni-goettingen.de/nbc.php?sel=archiv},
event_name = {31st Göttingen Neurobiology Conference},
event_place = {Göttingen, Germany},
state = {published},
author = {Sigala A. GR{sigala}{Department Physiology of Cognitive Processes}, Nielsen K{kristina}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 5551,
title = {Using 3-D human-monkey morphs to explore the boundaries of species dependent face-categories in humans},
journal = {Perception},
year = {2006},
month = {8},
volume = {35},
number = {ECVP Abstract Supplement},
pages = {207-208},
abstract = {Face perception has often been investigated with human faces differing in categories such as race or gender. Here, we investigate the perceptual border across species. We applied a method based on support vector machines to generate images of hybrid monkey - human faces (‘morphs‘) with different levels of human contribution. In the ‘explicit‘ experiment, we asked subjects to rate morphs at different morph levels as ‘humans‘ or ‘monkeys‘. We found that subjects rated the morphs as humans when they had a human contribution of at least 56%±3%. In the ‘implicit‘ experiment, we asked whether subjects could distinguish between successively presented morphs differing by ±10% morph level from a morph centre. By varying the morph centre value from 10% to 90%, we were able to measure subject&lsqu
o;s sensitivity to detect species differences along the human - monkey continuum. We found that the sensitivity of subjects to detect species differences was highest when morphs had a human contribution of 65%±3%. In summary, the human - monkey boundary does not lie at the midpoint of the human - monkey continuum, but tends to be shifted towards the human side. Our results reveal an asymmetry in the perception of human - monkey morphed faces, which may be species-specific and/or due to expertise.},
web_url = {http://www.perceptionweb.com/abstract.cgi?id=v060412},
event_name = {29th European Conference on Visual Perception},
event_place = {St. Petersburg, Russia},
state = {published},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Koch A, Nielsen KJ{kristina}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}

@Poster{ 5853,
title = {Inferior temporal cortex during real world vision},
year = {2006},
month = {6},
pages = {74},
abstract = {Much of current visual neuroscience is performed using standardized procedures. Most notably,
these generally include stimulus delivery using computer displays, the requirement of fixation,
repeated performance of experimental conditions and lengthy conditioning of animals on tasks to
allow for behavioral reports. Correlating neural responses with stimulus characteristics and
behavior lies at the heart of systems neuroscience. These controlled conditions have many
advantages, but at the same time can only represent an approximation of the processes that
occur during real world vision.
But how much are we missing under these constraints? Real world vision is characterized by eye
movements in three dimensions as observers fixate and track objects in the environment. What
are the characteristics of spike trains collected under such conditions and how do they differ from
those collected during task performance. How much can be said about neural activity by applying
the correlational approach to data acquired under these conditions? Does what we learn about
neural activity and selectivity during task performance generalize to real world vision? To begin to
address these questions, we have recorded extracellular activity of several inferior temporal
cortex neurons simultaneously while monkeys viewed face and object stimuli presented on a
computer monitor at the center of gaze during fixation. Then we record activity of the same
neurons during interaction with a human experimenter, while measuring the monkeys’ eye
position and recording the visual input using a camera. We compare about 5 minutes of activity
collected during these two conditions. Preliminary results suggest many IT neurons were
dynamically modulated during real world vision. Peak firing rates (eg at 200ms binwidth) tended
to be greater during real world vision than during task performance. Some IT neurons showed
markedly different interspike interval distributions in the two conditions.
Our findings suggest that a dynamic three dimensional visual environment may be a useful tool
for elucidating the function of visual neurons.},
web_url = {http://www.areadne.org/2006/},
event_name = {AREADNE 2006: Research in Encoding and Decoding of Neural Ensembles},
event_place = {Santorini, Greece},
state = {published},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Liebe S{sliebe}{Department Physiology of Cognitive Processes}, Nielsen K{kristina}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Reiner G{gregor}}
}

@Poster{ 5546,
title = {Learning mid-level motion features for the recognition of body movements},
journal = {Journal of Vision},
year = {2005},
month = {9},
volume = {5},
number = {8},
pages = {26},
abstract = {Body movements are characterized by specific sequences of complex optic flow patterns. Computational models for the perception of static shapes have demonstrated that recognition performance can be significantly improved by choosing an appropriate dictionary of mid-level shape-components (see abstract by Serre & Poggio, 2005). Preliminary results suggest that such shape-tuned units are consistent with recent physiological data collected in V4 (see abstract by Cadieu et al, 2005). We test if the visual recognition of complex body movements from optic flow might also benefit from optimized motion-component units.

METHOD: We employ a physiologically inspired learning algorithm for the optimization of mid-level motion detectors of a hierarchical model for the recognition of human actions (Giese & Poggio, 2003). In the proposed algorithm, competing units are associated with a memory trace that reflects their recent synaptic activity. The model is presented with movies showing a human action (i.e. walking): the trace from units that are behaviorally-relevant is increased while the trace from the others is decreased. Units whose memory trace falls below a critical threshold are randomly replaced.

RESULTS: When presented with movies showing human actions, the model generates a dictionary of mid-level motion-component units that lead to a significant improvement of the recognition performance. For the special case of walking, many of the units' preferred stimuli were characterized by horizontal opponent motion, consistent with a recent experimental study showing that opponent horizontal motion is a critical feature for the recognition of these stimuli (Casile & Giese, 2003).

CONCLUSION: Like for the categorization of static shapes, recognition performance for human actions is improved by choosing optimized mid-level motion features. In addition, the extracted features might predict receptive field properties of complex motion-selective neurons, e.g. in areas MT and MSTl.},
web_url = {http://www.journalofvision.org/5/8/26/},
event_name = {Fifth Annual Meeting of the Vision Sciences Society (VSS 2005)},
event_place = {Sarasota, FL, USA},
state = {published},
DOI = {10.1167/5.8.26},
author = {Sigala R{sigala}{Department Physiology of Cognitive Processes}, Serre T, Poggio T and Giese MA{giese}}
}

@Poster{ 5550,
title = {Mid-level motion features for the recognition of biological movements},
journal = {Perception},
year = {2005},
month = {8},
volume = {34},
number = {ECVP Abstract Supplement},
pages = {64},
abstract = {Recognition of biological motion probably needs the integration of form and motion information. For recognition and categorisation of complex static shapes, recognition performance can be significantly increased by optimisation of the extracted mid-level form features. Several algorithms for the learning of optimised mid-level features from image data have been proposed. It seems likely that the visual recognition of complex movements is also based on optimised features. Exploiting a new physiologically inspired algorithm and classical unsupervised learning methods, we have tried to determine mid-level motion features that are maximally useful for the recognition of body movements from image sequences. We optimised mid-level neural detectors in a hierarchical model for the recognition of human actions (Giese and Poggio, 2003 Nature Reviews Neuroscience 4 179 - 192) by unsupervised learning. Learning is based on a memory trace learning rule: Each detector is associated with a memory variable that increases when the detector is activated during correct classifications, and that decreases otherwise. Detectors whose memory variable falls below a critical threshold 'die', and are eliminated from the model. In addition, we tested a classical principal-components approach. The model is trained with movies showing different human actions, from which optic flow fields are computed. The tested learning algorithms extract mid-level motion features that lead to a substantial improvement of the recognition performance. For the special case of walking, many of the extracted motion features are characterised by horizontal opponent motion. This result is consistent with psychophysical data showing that opponent horizontal motion is a dominant mid-level feature that accounts for high recognition rates, even for strongly impoverished stimuli (Casile and Giese, 2005 Journal of Vision 5 348 - 360). As for the categorisation of static shapes, recognition performance for human actions is improved by choosing optimised mid-level features. The learned features might predict receptive field properties of complex motion-selective neurons (eg in area KO/V3B).},
web_url = {http://www.perceptionweb.com/abstract.cgi?id=v050627},
event_name = {28th European Conference on Visual Perception},
event_place = {A Coruña, Spain},
state = {published},
author = {Sigala RA{sigala}, Serre T, Poggio T and Casile A}
}

@Poster{ 5543,
title = {Physiologically inspired neural model for the prototype-referenced encoding of faces},
journal = {Journal of Vision},
year = {2004},
month = {8},
volume = {4},
number = {8},
pages = {213},
abstract = {Some psychological models for face recognition assume that faces are encoded as vectors in face spaces relative to an average face, or face prototype [T Valentine, Q J Exp Psychol A, 43, 161 (1991)]. So far it has been largely unclear how such a prototype-referenced encoding can be realized at a neural level. Recent electrophysiological data supports the relevance of such encoding in monkey visual cortex. Neurons in area IT, after training with human faces, show monotonic tuning with respect to the caricature level of face stimuli [D Leopold et al., Soc. of Neurosci., Poster 590.7 (2003)]. A neural model is presented that accounts for these electrophysiological results. The model consists of a hierarchy of layers with physiologically plausible neural feature detectors. The complexity of the extracted features increases along the hierarchy. Neurons on the highest level encode example views of faces. The tuning of these neurons is determined by the difference between the feature vector representing the test face, and an average feature vector that is computed from the previous history of stimulation. The neurons are tuned monotonically with respect to the length of the difference vector, and show angular tuning with respect to its direction in feature space. The model was tested with gray-level images generated with a morphable 3D face model [V Blanz, T Vetter, SIGGRAPH '99, 187–194 (1999)], replicating the stimulus set from the electrophysiological study. We conclude that prototype-referenced encoding, compared with the encoding in shape spaces with absolute coordinates, increases coding efficiency by optimally exploiting the available neural hardware.},
web_url = {http://www.journalofvision.org/4/8/213/},
event_name = {Fourth Annual Meeting of the Vision Sciences Society (VSS 2004)},
event_place = {Sarasota, FL, USA},
state = {published},
DOI = {10.1167/4.8.213},
author = {Giese MA{giese}, Sigala R{sigala}{Department Physiology of Cognitive Processes}, Wallraven C{walli} and Leopold D{davidl}{Department Physiology of Cognitive Processes}}
}

@Poster{ 5547,
title = {Physiologially Plausible Neuronal Model for Prototype-Referenced Encoding of Faces},
year = {2004},
month = {2},
volume = {7},
pages = {144},
abstract = {Conceptual models of face recognition have assumed that faces are encoded as points of an abstract
face space relative to an average face, or face prototype (e.g. [1]). So far it has been
largely unclear how such a prototype-referenced encoding of faces could be implemented
with real neurons. Recent electrophysiological evidence seems to support the relevance of
prototype-referenced encoding. Neurons in macque inferotemporal cortex, which have been
trained with human faces, tend to show a monotonic tuning with the caricature level of the
stimuli [2]. We present a neural model that accounts for these new electrophysiological results.
The hierarchical model consists of multiple layers of neural detectors modeling properties
of neurons in the dorsal visual processing stream. The rst layer models simple cells using Gabor
lters with with physiologically realistic parameters. A second layer combines responses
of Gabor lters that carry signicant information about a training stimuli into more complex
features. The complex features in the model are based on the Principal Components of the Gabor
responses, which could be extracted using simple Hebbian-like learning rules. The highest
hierarchy layer models neurons in area IT. The responses of these neural detectors increase
monotonically with the distance of the input feature vector, from the previous layer, and the
average feature vector over all training faces. In addition, neural detectors on the highest hierarchy
level show a broad tuning with resepect to the direction of the difference vector between
input feature vector and this average vector.
The model was tested with gray-level images that were generated using a morphable 3D
face model [3]. The model was trained with 98 randomly chosen faces from a data basis with
200 faces. It was tested with caricatures and anti-caricatures of 4 selected faces. In addition we
tested lateral caricatures of the faces, which lie on curves in face space that connect the four
selected example faces. Exactly the same stimuli had been used in the electrophysiological
experiments [2].
After training, a signicant number of the neural units on the highest level of the model
show a monotonic tuning with the caricature level of the faces, and a moderate tuning with
respect to facial identity, consistent with the electriophysiological results. The model provides
a physiologically plausible concrete neural implementation of face spaces. Future work will
explore its computational properties and coding efciency in comparison with classical neural
models for face recognition.},
web_url = {http://www.twk.tuebingen.mpg.de/twk04/index.php},
event_name = {7th Tübingen Perception Conference (TWK 2004)},
event_place = {Tübingen, Germany},
state = {published},
author = {Sigala A. GR{sigala}{Department Physiology of Cognitive Processes}, Leopold D{davidl}{Department Physiology of Cognitive Processes}, Wallraven C{walli} and Giese MA{giese}}
}

@Poster{ 3289,
title = {Physiologically plausible model for the prototype-referenced encoding of faces},
year = {2004},
state = {published},
author = {Giese MA{giese}, Sigala R{sigala}, Wallraven C{walli} and Leopold DA{davidl}}
}

@Conference{ 5000,
title = {Decoding the human/monkey face category boundary from the macaque inferior-temporal (IT) cortex using 3D human/monkey morphed faces},
year = {2007},
month = {11},
volume = {37},
number = {554.8},
abstract = {Ambiguous stimuli constitute a powerful method to dissociate between the physical properties of the stimuli and their representation in the brain. Following this idea, we applied a new computer-vision algorithm based on Support-Vector-Machines (SVMs) to create three-dimensional morphed faces (linear interpolated) between humans and monkeys in order to investigate how species-dependent face information is encoded in the inferior-temporal (IT) cortex of the macaque brain. Previous psychophysical experiments using these stimuli have shown that human subjects tend to classify ambiguous morphs as discrete instances of the human/monkey categories (‘categorical perception’). Moreover, subjects draw the category boundary closer to their own species (at approximately 60%human/40% monkey).
We recorded the single-unit-activity (SUA) of 118 neurons and the local field potential (LFP) at 58 sites of the IT cortex of one macaque monkey during fixation of these morphed stimuli. Out of a total of 118 single units, 85% were visually responsive, 23% were selective to faces, 12% selective to monkeys and 14% to humans, according to standard criteria. To analyze the population activity, we trained different classifiers (k-Nearest Neighbor, Support vector Machines, K-Means) to learn the representation (SUA and LFPs) of human and monkey faces and tested them with the ambiguous stimuli. We found that, symmetric to the findings in humans, ambiguous faces are categorized by the pattern classifiers in a manner implying a categorical representation of the faces. Furthermore, the classifiers drew the category boundary closer to the monkey category (at approximately 40%human/60% monkey) for both kinds of neural signals.
In contrast to the linear change of the morphed faces, our preliminary results showed that the neural representation of the species information is nonlinear. This nonlinearity suggests an ‘own-species’ advantage in the encoding of face stimuli. Consistent with learning theories, this advantage seems to be better reflected in our data by a sharper tuning of the monkey-selective cells compared to the human-selective, and not by a difference in the number of cells.},
web_url = {http://www.sfn.org/am2007/},
event_name = {37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007)},
event_place = {San Diego, CA, USA},
state = {published},
author = {Sigala Alanis GR{sigala}{Department Physiology of Cognitive Processes}, Nielsen KJ{kristina}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}