Probabilistic brains: knowns and unknowns

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Abstract

There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.

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Figure 1: The visuo-haptic multisensory experiment of Ernst and Banks11.
Figure 2: Probabilistic population code using a basis function decomposition of the log probability.
Figure 3: Taking a product of likelihood functions with probabilistic population codes.
Figure 4: Neural network for Chinese character identification.
Figure 5: Incremental structural learning.

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Acknowledgements

P.E.L. is supported by the Gatsby Charitable Foundation, W.J.M. by National Eye Institute grant R01EY020958-01, National Science Foundation grant IIS-1132009 (Collaborative Research in Computational Neuroscience), and Army Research Office grant W911NF-12-1-0262, and A.P. by National Science Foundation grant #BCS0446730, Multi-University Research Initiative grant #N00014-07-1-0937, National Institute on Drug Abuse grants #BCS0346785, the Swiss National Fund (31003A 143707) and a research grant from the James S. McDonnell Foundation.

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Correspondence to Alexandre Pouget.

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Pouget, A., Beck, J., Ma, W. et al. Probabilistic brains: knowns and unknowns. Nat Neurosci 16, 1170–1178 (2013). https://doi.org/10.1038/nn.3495

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