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Dr. Jakob Macke

Adresse: Spemannstrasse 41
72074 Tübingen
Tel.: 07071 601 1721
E-Mail: jakob.macke

 

Bild von Macke, Jakob, Dr.

Jakob Macke

Position: Leiter Forschungsgruppe  Abteilung: 

Since 05/2012: Junior research group leader, MPI for Biological Cybernetics and Bernstein Center for Computational Neuroscience

04/2010-04/2012: Postdoc at the Gatsby Computational Neuroscience Unit, University College London, UK

10/2005-04/2010: PhD student, MPI for Biological Cybernetics

06/2005: Master in Mathematics, University of Oxford, UK

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Artikel (20):

Schütt HH, Harmeling S, Macke JH und Wichmann FA (Mai-2016) Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data Vision Research 122 105–123.
Panzeri S, Macke JH, Gross J und Kayser C (März-2015) Neural population coding: combining insights from microscopic and mass signals Trends in Cognitive Sciences 19(3) 162–172.
Küffner R, Zach N, Norel R, Hawe J, Schoenfeld D, Wang L, Li G, Fang L, Mackey L, Hardiman O, Cudkowicz M, Sherman A, Ertaylan G, Grosse-Wentrup M, Hothorn T, van Ligtenberg J, Macke JH, Meyer T, Schölkopf B, Tran L, Vaughan R, Stolovitzky G und Leitner ML (Januar-2015) Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression Nature Biotechnology 33(1) 51-57.
Fründ I, Wichmann FA und Macke J (Juni-2014) Quantifying the effect of intertrial dependence on perceptual decisions Journal of Vision 14(7:9) 1-16.
Watanabe M, Bartels A, Macke JH, Murayama Y und Logothetis NK (November-2013) Temporal Jitter of the BOLD Signal Reveals a Reliable Initial Dip and Improved Spatial Resolution Current Biology 23(21) 2146–2150.
Macke JH, Murray I und Latham PE (August-2013) Estimation bias in maximum entropy models Entropy 15(8) 3109-3219.
Haefner RM, Gerwinn S, Macke JH und Bethge M (Februar-2013) Inferring decoding strategies from choice probabilities in the presence of correlated variability Nature Neuroscience 16(2) 235–242.
Schwartz G, Macke J, Amodei D, Tang H und Berry MJ (August-2012) Low Error Discrimination using a Correlated Population Code Journal of Neurophysiology 108(4) 1069-1088.
Buesing L, Macke JH und Sahani M (März-2012) Learning stable, regularised latent models of neural population dynamics Network 23(1-2) 24-47.
Macke J, Berens P und Bethge M (Juli-2011) Statistical analysis of multi-cell recordings: linking population coding models to experimental data Frontiers in Computational Neuroscience 5(35) 1-2.
Macke JH, Opper M und Bethge M (Mai-2011) Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity Physical Review Letters 106(20) 1-4.
Macke JH, Gerwinn S, White LW, Kaschube M und Bethge M (Mai-2011) Gaussian process methods for estimating cortical maps NeuroImage 56(2) 570-581.
Gerwinn S, Macke JH und Bethge M (Februar-2011) Reconstructing stimuli from the spike-times of leaky integrate and fire neurons Frontiers in Neuroscience 5(1) 1-16.
Lyamzin DR, Macke JH und Lesica NA (Oktober-2010) Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations Frontiers in Computational Neuroscience 4(144) 1-11.
Macke JH und Wichmann FA (Mai-2010) Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces Journal of Vision 10(5:22) 1-24.
Gerwinn S, Macke J und Bethge M (April-2010) Bayesian inference for generalized linear models for spiking neurons Frontiers in Computational Neuroscience 4(12) 1-17.
Gerwinn S, Macke JH und Bethge M (Oktober-2009) Bayesian population decoding of spiking neurons Frontiers in Computational Neuroscience 3(21) 1-14.
Macke JH, Berens P, Ecker AS, Tolias AS und Bethge M (Februar-2009) Generating Spike Trains with Specified Correlation Coefficients Neural Computation 21(2) 397-423.
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Ku S-P, Gretton A, Macke J und Logothetis NK (September-2008) Comparison of Pattern Recognition Methods in Classifying High-resolution BOLD Signals Obtained at High Magnetic Field in Monkeys Magnetic Resonance Imaging 26(7) 1007-1014.
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Macke JH, Maack N, Gupta R, Denk W, Schölkopf B und Borst A (Januar-2008) Contour-propagation Algorithms for Semi-automated Reconstruction of Neural Processes Journal of Neuroscience Methods 167(2) 349-357.
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Beiträge zu Tagungsbänden (12):

Park M, Bohner G und Macke J (2016) Unlocking neural population non-stationarity using a hierarchical dynamics model In: Advances in Neural Information Processing Systems 28, , Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015), Curran, Red Hook, NY, USA, 145-153.
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Putzky P, Franzen F, Bassetto G und Macke JH (2015) A Bayesian model for identifying hierarchically organised states in neural population activity In: Advances in Neural Information Processing Systems 27, , Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014), Curran, Red Hook, NY, USA, 3095-3103.
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Archer EW, Koster U, Pillow JW und Macke JH (2015) Low-dimensional models of neural population activity in sensory cortical circuits In: Advances in Neural Information Processing Systems 27, , Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014), Curran, Red Hook, NY, USA, 343-351.
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Turaga SC, Buesing L, Packer AM, Dalgleish H, Pettit N, Hausser M und Macke JH (2014) Inferring neural population dynamics from multiple partial recordings of the same neural circuit In: Advances in Neural Information Processing Systems 26, , Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS 2013), Curran, Red Hook, NY, USA, 539-547.
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Buesing L, Macke JH und Sahani M (April-2013) Spectral learning of linear dynamics from generalised-linear observations with application to neural population data In: Advances in Neural Information Processing Systems 25, , Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS 2012), Curran, Red Hook, NY, USA, 1691-1699.
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Macke JH, Büsing L, Cunningham JP, Yu BM, Shenoy KV und Sahani M (Januar-2012) Empirical models of spiking in neural populations In: Advances in Neural Information Processing Systems 24, , Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Curran, Red Hook, NY, USA, 1350-1358.
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Macke JH, Murray I und Latham P (Januar-2012) How biased are maximum entropy models? In: Advances in Neural Information Processing Systems 24, , Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Curran, Red Hook, NY, USA, 2034-2042.
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Macke JH, Gerwinn S, Kaschube M, White LE und Bethge M (April-2010) Bayesian estimation of orientation preference maps In: Advances in Neural Information Processing Systems 22, , 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Curran, Red Hook, NY, USA, 1195-1203.
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Gerwinn S, Macke J, Seeger M und Bethge M (September-2008) Bayesian Inference for Spiking Neuron Models with a Sparsity Prior In: Advances in neural information processing systems 20, , Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 529-536.
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Macke JH, Zeck G und Bethge M (September-2008) Receptive Fields without Spike-Triggering In: Advances in neural information processing systems 20, , Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 969-976.
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Laub J, Macke JH, Müller K-R und Wichmann FA (September-2007) Inducing Metric Violations in Human Similarity Judgements In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 777-784.
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Bethge M, Gerwinn S und Macke JH (Februar-2007) Unsupervised learning of a steerable basis for invariant image representations In: Human Vision and Electronic Imaging XII, , SPIE Human Vision and Electronic Imaging Conference 2007, SPIE, Bellingham, WA, USA, 1-12, Series: Proceedings of the SPIE ; 6492.
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Beiträge zu Büchern (2):

Macke JH: Electrophysiology Analysis, Bayesian, 1078-1082. In: Encyclopedia of Computational Neuroscience, (Ed) D. Jaeger, Springer, New York, NY, USA, (2015).
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Macke JH, Buesing L und Sahani M: Estimating State and Parameters in State Space Models of Spike Trains, 137-159. In: Advanced State Space Methods for Neural and Clinical Data, (Ed) Z. Chen, Cambridge University Press, Cambridge, UK, (2015).
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Technische Berichte (2):

Park M und Macke JH: Hierarchical models for neural population dynamics in the presence of non-stationarity, -, (Januar-2015).
Macke JH, Opper M und Bethge M: The effect of pairwise neural correlations on global population statistics, 183, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (März-2009).
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Poster (41):

Bassetto G und Macke J (September-21-2016): Full Bayesian inference for model-based receptive field estimation, with application to primary visual cortex, Bernstein Conference 2016, Berlin, Germany.
Nonnenmacher M, Buesing L, Speiser A, Turaga S und Macke JH (Februar-27-2016): Stitching neural activity in space and time: theory and practice, Computational and Systems Neuroscience Meeting (COSYNE 2016), Salt Lake City, UT, USA.
Nonnenmacher M, Behrens C, Berens P, Bethge M und Macke JH (Oktober-20-2015): Correlations and signatures of criticality in neural population models, 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), Chicago, IL, USA.
Czubayko U, Bassetto G, Narayanan RT, Oberlaender M, Macke JH und Kerr JND (Oktober-18-2015): Anatomical basis of spiking correlation in upper layers of somatosensory cortex, 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), Chicago, IL, USA.
Rulla S, Ng B, Macke J, Wallace D, Sawinski J und Kerr J (Oktober-18-2015): Two-photon imaging of neuronal populations in the primary visual cortex representation of the overhead visual field, 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), Chicago, IL, USA.
Bassetto G, Sandhaeger F, Ecker A und Macke JH (September-16-2015): A statistical characterization of neural population responses in V1, Bernstein Conference 2015, Heidelberg, Germany.
Schütt H, Harmeling S, Macke J und Wichmann F (September-2015): Psignifit 4: Pain-free Bayesian Inference for Psychometric Functions, 15th Annual Meeting of the Vision Sciences Society (VSS 2015), St. Pete Beach, FL, USA, Journal of Vision, 15(12) 474.
Nonnenmacher M, Behrens C, Berens P, Bethge M und Macke J (März-7-2015): Correlations and signatures of criticality in neural population models, Computational and Systems Neuroscience Meeting (COSYNE 2015), Salt Lake City, UT, USA.
Nienborg H und Macke JH (November-17-2014): Using sequential dependencies in neural activity and behavior to dissect choice related activity in V2, 44th Annual Meeting of the Society for Neuroscience (Neuroscience 2014), Washington, DC, USA.
Archer E, Pillow J und Macke J (Oktober-2014): Low Dimensional Dynamical Models of Neural Populations with Common Input, 15th Conference of Junior Neuroscientists of Tübingen (NeNa 2014), Schramberg, Germany.
Archer E, Pillow JW und Macke JH (September-3-2014): Low-dimensional dynamical neural population models with shared stimulus drive, Bernstein Conference 2014, Göttingen, Germany.
Nienborg H und Macke JH (September-3-2014): Using sequential dependencies in neural activity and behavior to dissect choice related activity in V2, Bernstein Conference 2014, Göttingen, Germany.
Schütt H, Harmeling S, Macke J und Wichmann F (August-2014): Pain-free bayesian inference for psychometric functions, 37th European Conference on Visual Perception (ECVP 2014), Beograd, Serbia, Perception, 43(ECVP Abstract Supplement) 162.
Schütt H, Harmeling S, Macke J und Wichmann F (August-2014): Pain-free Bayesian inference for psychometric functions, 2014 European Mathematical Psychology Group Meeting (EMPG), Tübingen, Germany.
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Last updated: Dienstag, 18.11.2014