A variational nonparametric bayesian approach for inferring rat hippocampal population codes

Author

Zhe Chen ; Wilson, M.A.

Author_Institution

Dept. of Brain & Cognitive Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA

fYear

2013

fDate

3-7 July 2013

Firstpage

7092

Lastpage

7095

Abstract

Rodent hippocampal population codes represent important spatial information of the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes. Specifically, we develop an infinite hidden Markov model (iHMM) and variational Bayes (VB) inference method to analyze rat hippocampal ensemble spike activity. We demonstrate the effectiveness of our approach using an open field navigation example and discuss the significance/implications of our results.

Keywords

Bayes methods; brain models; hidden Markov models; inference mechanisms; topology; variational techniques; infinite hidden Markov model; navigation; neural representation; rat hippocampal population codes; rodent; spatial information; spatial topology; variational Bayes inference method; variational nonparametric Bayesian approach; Bayes methods; Hidden Markov models; Robustness; Rodents; Sociology; Statistics; Topology; Spatial representation; hippocampal population codes; infinite hidden Markov model; variational Bayes;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6611192

Filename

6611192