Matthew Chalk

Affiliations: 
2018- Institut de la Vision Sorbonne Université, Paris, France 
Area:
Computational neuroscience
Website:
https://matthewjchalk.wixsite.com/mysite
Google:
"https://scholar.google.fr/citations?user=eUTdJEcAAAAJ&hl=en&oi=ao"
Mean distance: 15.06 (cluster 17)
 
SNBCP
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Publications

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Despotović D, Joffrois C, Marre O, et al. (2024) Encoding surprise by retinal ganglion cells. Plos Computational Biology. 20: e1011965
Goldin MA, Virgili S, Chalk M. (2023) Scalable Gaussian process inference of neural responses to natural images. Proceedings of the National Academy of Sciences of the United States of America. 120: e2301150120
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
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