Document Detail

Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons.
MedLine Citation:
PMID:  21395435     Owner:  NLM     Status:  Publisher    
A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., information-maximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.
Jeffrey Beck; Vikranth R Bejjanki; Alexandre Pouget
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-3-11
Journal Detail:
Title:  Neural computation     Volume:  -     ISSN:  1530-888X     ISO Abbreviation:  -     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-3-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Gatsby Computational Neuroscience Unit, University College London, London, WC1N 3AR, U.K.
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