Journal
CURRENT OPINION IN NEUROBIOLOGY
Volume 46, Issue -, Pages 120-126Publisher
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2017.08.001
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Funding
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7) under REA [291734]
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Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible.
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