Directed information between connected leaky integrate-and-fire neurons

Nima Soltani, Andrea Goldsmith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper we develop a method of finding the directed information of a particular neural topology analytically. We assume a leaky integrate-and-fire (LIF) neuron model, and calculate the directed information between the spike train of an input neuron to the LIF model and the corresponding spike train generated by the LIF model based on this input. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We then use this model to find the directed information analytically. Additionally, we show how the stationary distribution and transition probabilities of the Markov model can be found using parameters of the LIF neuron. This modeling technique can thus be used to obtain the value of the directed information in a particular neuronal topology.

Original languageEnglish (US)
Title of host publication2014 IEEE International Symposium on Information Theory, ISIT 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1291-1295
Number of pages5
ISBN (Print)9781479951864
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 IEEE International Symposium on Information Theory, ISIT 2014 - Honolulu, HI, United States
Duration: Jun 29 2014Jul 4 2014

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other2014 IEEE International Symposium on Information Theory, ISIT 2014
CountryUnited States
CityHonolulu, HI
Period6/29/147/4/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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