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Maximum Entropy Approaches to Living Neural Networks

Department of Physics, Indiana University, 727 East 3rd Street, Bloomington, IN, 47405-7105, USA
Indiana University Program in Neuroscience, Bloomington, IN 47405, USA
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059, Krakow, Poland
Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
Authors to whom correspondence should be addressed.
Entropy 2010, 12(1), 89-106;
Received: 11 December 2009 / Revised: 6 January 2010 / Accepted: 11 January 2010 / Published: 13 January 2010
Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research. View Full-Text
Keywords: maximum entropy; neural network; multielectrode array; Ising model maximum entropy; neural network; multielectrode array; Ising model
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MDPI and ACS Style

Yeh, F.-C.; Tang, A.; Hobbs, J.P.; Hottowy, P.; Dabrowski, W.; Sher, A.; Litke, A.; Beggs, J.M. Maximum Entropy Approaches to Living Neural Networks. Entropy 2010, 12, 89-106.

AMA Style

Yeh F-C, Tang A, Hobbs JP, Hottowy P, Dabrowski W, Sher A, Litke A, Beggs JM. Maximum Entropy Approaches to Living Neural Networks. Entropy. 2010; 12(1):89-106.

Chicago/Turabian Style

Yeh, Fang-Chin; Tang, Aonan; Hobbs, Jon P.; Hottowy, Pawel; Dabrowski, Wladyslaw; Sher, Alexander; Litke, Alan; Beggs, John M. 2010. "Maximum Entropy Approaches to Living Neural Networks" Entropy 12, no. 1: 89-106.

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