Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10944
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Main Title: Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
Author(s): Donner, Christian
Obermayer, Klaus
Shimazaki, Hideaki
Type: Article
Language Code: en
Abstract: The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.
URI: https://depositonce.tu-berlin.de/handle/11303/12070
http://dx.doi.org/10.14279/depositonce-10944
Issue Date: 17-Jan-2017
Date Available: 26-Nov-2020
DDC Class: 610 Medizin und Gesundheit
Subject(s): neural population
neuron
approximation method
neural network
Sponsor/Funder: DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systemen
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Publisher Place: San Francisco, Calif.
Volume: 13
Issue: 1
Article Number: e1005309
Publisher DOI: 10.1371/journal.pcbi.1005309
EISSN: 1553-7358
ISSN: 1553-734X
Appears in Collections:FG Neuronale Informationsverarbeitung » Publications

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