Jakob H. Macke, PhD

Affiliations: 
Gatsby Computational Neuroscience Unit University College London, London, United Kingdom 
Area:
Computation and Theory, Vision
Website:
http://www.kyb.mpg.de/~jakob
Google:
"Jakob Macke"
Mean distance: 13.93 (cluster 17)
 
SNBCP
Cross-listing: Computational Biology Tree

Parents

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Karel Svoboda research assistant 2004-2004 CSHL
 (Undergraduate Research Program)
Bernhard Schölkopf grad student MPI for Biological Cybernetics
Matthias Bethge grad student 2005-2010 MPI for Biological Cybernetics
Maneesh Sahani post-doc 2010- UCL

Children

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Pedro J. Gonçalves grad student
Piotr Sokol grad student
Mijung Park post-doc 2014-2014 MPI Tuebingen
BETA: Related publications

Publications

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Berens P, Freeman J, Deneux T, et al. (2018) Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. Plos Computational Biology. 14: e1006157
Nonnenmacher M, Behrens C, Berens P, et al. (2017) Signatures of criticality arise from random subsampling in simple population models. Plos Computational Biology. 13: e1005718
Schütt HH, Harmeling S, Macke JH, et al. (2016) Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research
Panzeri S, Macke JH, Gross J, et al. (2015) Neural population coding: combining insights from microscopic and mass signals. Trends in Cognitive Sciences. 19: 162-72
Küffner R, Zach N, Norel R, et al. (2015) Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nature Biotechnology. 33: 51-7
Macke JH, Buesing L, Sahani M. (2015) Estimating state and parameters in state space models of spike trains Advanced State Space Methods For Neural and Clinical Data. 137-159
Park M, Bohner G, Macke JH. (2015) Unlocking neural population non-stationarity using a hierarchical dynamics model Advances in Neural Information Processing Systems. 2015: 145-153
Fründ I, Wichmann FA, Macke JH. (2014) Quantifying the effect of intertrial dependence on perceptual decisions. Journal of Vision. 14
Archer E, Köster U, Pillow J, et al. (2014) Low-dimensional models of neural population activity in sensory cortical circuits Advances in Neural Information Processing Systems. 1: 343-351
Putzky P, Franzen F, Bassetto G, et al. (2014) A Bayesian model for identifying hierarchically organised states in neural population activity Advances in Neural Information Processing Systems. 4: 3095-3103
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