Matthew Chalk

2018- Institut de la Vision Sorbonne Université, Paris, France 
Computational neuroscience
Mean distance: 15.06 (cluster 17)
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Chalk M, Tkacik G, Marre O. (2021) Inferring the function performed by a recurrent neural network. Plos One. 16: e0248940
Chalk M, Marre O, Tkačik G. (2018) Toward a unified theory of efficient, predictive, and sparse coding. Proceedings of the National Academy of Sciences of the United States of America. 115: 186-191
Chalk M, Masset P, Gutkin B, et al. (2017) Sensory noise predicts divisive reshaping of receptive fields. Plos Computational Biology. 13: e1005582
Chalk M, Gutkin B, Denève S. (2016) Neural oscillations as a signature of efficient coding in the presence of synaptic delays. Elife. 5
Deneve S, Chalk M. (2016) Efficiency turns the table on neural encoding, decoding and noise. Current Opinion in Neurobiology. 37: 141-148
Chalk M, Gutkin B, Denève S. (2016) Author response: Neural oscillations as a signature of efficient coding in the presence of synaptic delays Elife
Chalk M, Murray I, Seriès P. (2013) Attention as reward-driven optimization of sensory processing. Neural Computation. 25: 2904-33
Gekas N, Chalk M, Seitz AR, et al. (2013) Complexity and specificity of experimentally-induced expectations in motion perception. Journal of Vision. 13
Chalk M, Seitz AR, Seriès P. (2010) Rapidly learned stimulus expectations alter perception of motion. Journal of Vision. 10: 2
Chalk M, Herrero JL, Gieselmann MA, et al. (2010) Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1. Neuron. 66: 114-25
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