Neural Systems Analysis

Max Planck Research Group

We seek to understand how neural networks perform complex computations to process sensory inputs.

Publications

Nonnenmacher, M., Turaga, S.C. & Macke, J.H. (2018) “Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations” In: Advances in Neural Information Processing Systems 30: 31st Conference on Neural Information Processing Systems (NIPS 2017)

Lueckmann, J., Goncalves, P., Bassetto, G., Oecal, K., Nonnenmacher, M. & Macke, J.H. (2018) “Flexible statistical inference for mechanistic models of neural dynamics” In: Advances in Neural Information Processing Systems 30: 31st Conference on Neural Information Processing Systems (NIPS 2017)

Speiser, A., Jinyao, Y., Archer, E., Buesing, L., Turaga, S.C. & Macke, J.H. (2018) “Fast amortized inference of neural activity from calcium imaging data with variational autoencoders” In: Advances in Neural Information Processing Systems 30: 31st Conference on Neural Information Processing Systems (NIPS 2017)

Nonnenmacher, M., Behrens, C., Berens, P., Bethge, M. & Macke, J.H. (2017) “Signatures of criticality arise from random subsampling in simple population models” PLoS Comput. Biol. 13, e1005886

Schütt, H.H., Harmeling, S., Macke, J.H. &  Wichmann, F.A. (2016) "Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data" Vision Res. 122, 105-123

Nonnenmacher, M., Behrens, C., Berens, P., Bethge, M. & Macke, J.H. (2016) "Signatures of criticality arise in simple neural population models with correlations" Arxiv preprint, 1603.00097

Park, M., Bohner, G. & Macke, J.H. (2016) "Unlocking neural population non-stationarities using hierarchical dynamics models" Advances in Neural Information Processing Systems 28: 29th Conference on Neural Information processing Systems (NIPS2015), 5790

Archer, E.W., Koster, U., Pillow, J. & Macke, J.H. (2015) "Low-dimensional models of neural population activity in sensory cortical circuits" Advances in Neural Information Processing Systems 27: 28th Conference on Neural Information Processing Systems (NIPS 2014), 343 - 351

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, J.H., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G. & Leitner, M.L. (2015) "Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression" Nat. Biotechnol 33, 51 – 57

Macke, J.H., Buesing, L. & Sahani, M. (2015) "Estimating state and Parameters in state space Models of Spike trains" In: Chen, Z. (ed.): Advanced State Space Methods for Neural and Clinical Data (Cambridge, UK: Cambridge University Press), 137 – 156

Panzeri, S., Macke, J.H., Gross, J. & Kayser, C. (2015) "Neural population coding: combining insights from microscopic and mass signals" Trends Cogn. Sci. 19, 162 - 172

Putzky, P., Franzen, F., Bassetto, G. & Macke, J.H.. (2015) "A Bayesian model for identifying hierarchically organised states in neural population activity" Advances in Neural Information Processing Systems 27: 28th Conference on Neural Information Processing Systems (NIPS 2014), 3095 - 3103

Fründ, I., Wichmann, F. A. & Macke, J.H. (2014) "Quantifying the effect of intertrial dependence on perceptual decisions" J. Vis. 14, 9

Macke, J.H. (2014) "Electrophysiology analysis bayesian" In: Jaeger, D. (ed.): Encyclopedia of Computational Neuroscience (New York: Springer), 1 – 5

Turag, S., Buesing, L., Packer, A.M., Dalgleish, H., Pettit, N., Hausser, M. & Macke, J.H. (2014) "Inferring neural population dynamics from multiple partial recordings of the same neural circuit" Advances in Neural Information Processing Systems 26 (NIPS 2013), 539 - 547

Buesing, L., Macke, J.H. & Sahani, M. (2013) "Spectral learning of linear dynamics from generalised-linear observations with application to neural population data" Advances in Neural Information Processing Systems 25: 26th Conference on Neural Information Processing Systems (NIPS 2012), 1691 - 1699

Haefner, R.M., Gerwinn, S., Macke, J.H. & Bethge, M. (2013) "Inferring decoding strategies from choice probabilities in the presence of correlated variability" Nat. Neurosci. 16, 235 - 242

Macke, J.H., Murray, I. & Latham, P.E. (2013) "Estimation Bias in Maximum Entropy Models" Entropy 15, 3109 - 3129

Watanabe, M., Bartels, A., Macke, J.H., Murayama, Y. & Logothetis, N.K. (2013) "Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution" Curr. Bio. 23, 2146 - 2150

Büsing, L., Macke, J.H. & Sahani, M. (2012) "Learning stable, regularised latent models of neural population dynamics" Network 23, 24 - 47

Macke, J.H., Buesing, L., Cunningham, J.P., Yu, B.M., Shenoy, K.V. & Sahani, M. (2012) "Empirical models of spiking in neural populations" Advances in Neural Information Processing Systems 24: 25th conference on Neural Information Processing Systems (NIPS 2011), 1350 – 1358

Macke, J.H., Murray, I. & Latham, P (2012) "How biased are maximum entropy models?" Advances in Neural Information Processing Systems 24: 25th Conference on Neural Information Processing Systems (NIPS 2011), 2034 - 2042

Schwartz, G., Macke, J.H., Amodei, D., Tang, H. & Berry, M.J. (2012) "Low error discrimination using a correlated population code" J. Neurophys. 108, 1069 – 1088

Gerwinn, S., Macke, J H. & Bethge, M. (2011) "Reconstructing stimuli from the spike times of leaky integrate and fire neurons" Front Neurosci 5, 1 - 16

Macke, J.H., Berens, P. & Bethge, M. (2011) "Statistical analysis of multi-cell recordings: linking population coding models to experimental data" Front. Comput. Neurosci. 5, 1 - 2

Lyamzin, D.R., Macke, J.H. &  Lesica, N.A. (2010) "Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations" Front. Comput. Neurosci. 4, 144

Macke, J.H. &  Wichmann, F.A. (2010) "Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces" J. Vis. 10, 22

Gerwinn, S., Macke, J.H. &  Bethge, M. (2009) "Bayesian population decoding of spiking neurons" Front. Comput. Neurosci. 3, 1-28

Macke, J.H., Berens, P., Ecker, A.S., Tolias, A.S. &  Bethge, M. (2009) "Generating spike trains with specified correlation coefficients" Neur. Comput. 21, 397-423

Ku, S.P., Gretton, A., Macke, J.H. &  Logothetis, N.K. (2008) "Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys" Magn. Reson. Imaging 26, 1007-1014

Macke, J.H., Maack, N., Gupta, R., Denk, W., Schölkopf, B. &  Borst, A. (2008) "Contour-propagation algorithms for semi-automated reconstruction of neural processes" J. Neurosci. Methods 167, 349-357