Abstract：
:It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors.
journal_name
Neuronjournal_title
Neuronauthors
Pitkow X,Angelaki DEdoi
10.1016/j.neuron.2017.05.028subject
Has Abstractpub_date
2017-06-07 00:00:00pages
943-953issue
5eissn
0896-6273issn
1097-4199pii
S0896-6273(17)30466-Xjournal_volume
94pub_type
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