title: An information transmission measure for the analysis of effective connectivity among cortical neurons.
abstract: We present a methodology for detecting effective connections between simultaneously recorded neurons using an information transmission measure to identify the presence and direction of information flow from one neuron to another. Using simulated and experimentally-measured data, we evaluate the performance of our proposed method and compare it to the traditional transfer entropy approach. In simulations, our measure of information transmission outperforms transfer entropy in identifying the effective connectivity structure of a neuron ensemble. For experimentally recorded data, where ground truth is unavailable, the proposed method also yields a more plausible effective connectivity structure than transfer entropy.
authors.affiliation: Biomedical Engineering Department at the University of Rochester, Rochester, NY, USA. Andrew_Law@urmc.rochester.edu
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Law
authors.forename: Gaurav
authors.initials: G
authors.lastname: Sharma
authors.forename: Marc H
authors.initials: MH
authors.lastname: Schieber
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D007257:Information Theory; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*