Using Markov Properties of ECoG Signals to Infer Neuron Connectivity

Yonathan Murin, Andrea Goldsmith

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

Abstract

Quantifying the causal associations between Electrocorticography (ECoG) recordings has multiple applications in neuroscience. In this work we study the Markov properties of ECoG recordings and their impact on estimating causal influences between these signals. We show that accurate estimation of the causal influence requires knowledge of the Markov order of the considered recordings. Since in general the Markov orders are unknown, they must be estimated from the data before the causal associations are estimated. To address this challenge we propose a data-driven method for estimating these Markov orders.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages671-675
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Externally publishedYes
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period10/28/1810/31/18

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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