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Approximate Inference Method for Dynamic Interactions in Larger Neural Populations

  • Christian Donner
  • Hideaki ShimazakiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

The maximum entropy method has been successfully employed to explain stationary spiking activity of a neural population by using fewer features than the number of possible activity patterns. Modeling network activity in vivo, however, has been challenging because features such as spike-rates and interactions can change according to sensory stimulation, behavior, or brain state. To capture the time-dependent activity, Shimazaki et al. (PLOS Comp Biol, 2012) previously introduced a state-space framework for the latent dynamics of neural interactions. However, the exact method suffers from computational cost; therefore its application was limited to only \({\sim }15\) neurons. Here we introduce the pseudolikelihood method combined with the TAP or Bethe approximation to the state-space model, and make it possible to estimate dynamic pairwise interactions of up to 30 neurons. These analytic approximations allow analyses of time-varying activity of larger networks in relation to stimuli or behavior.

Keywords

Marginal Likelihood Neural Interaction Bethe Approximation Recursive Bayesian Estimation Exponential Family Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

CD acknowledges T. Toyoizumi for hosting his stay in RIKEN Brain Science Institute and K. Obermayer for valuable ideas and discussions. The custom-made Python programs were developed based on the code originally written by T. Sharp. CD was supported by the Deutsche Forschungsgemeinschaft GRK1589/2.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Bernstein Center for Computational NeuroscienceBerlinGermany
  2. 2.Neural Information Processing GroupTechnische Universität BerlinBerlinGermany
  3. 3.RIKEN Brain Science InstituteSaitamaJapan

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