Dynamics of a fully stochastic discretized neuronal model with excitatory and inhibitory neurons
Berning, Stephen R
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https://hdl.handle.net/2142/88189
Description
Title
Dynamics of a fully stochastic discretized neuronal model with excitatory and inhibitory neurons
Author(s)
Berning, Stephen R
Issue Date
2015-07-16
Director of Research (if dissertation) or Advisor (if thesis)
DeVille, Lee
Doctoral Committee Chair(s)
Rapti, Zoi
Committee Member(s)
Kirkpatrick, Kay L
Zharnitsky, Vadim
Department of Study
Mathematics
Discipline
Mathematics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
neural network
neuronal network
inhibitory neurons
inhibition
non-monotonic
synchrony
mean-field analysis
integrate-and-fire
limit theorem
Abstract
We consider here an extension and generalization of the stochastic
neuronal network model developed by DeVille et al.; their model
corresponded to an all-to-all network of discretized
integrate-and-fire excitatory neurons where synapses are
failure-prone. It was shown that this model exhibits different
metastable phases of asynchronous and synchronous behavior, since the
model limits on a mean-field deterministic system with multiple
attractors. Our work investigates adding inhibition into the
model. The new model exhibits the same metastable phases, but also
exhibits new non-monotonic behavior that was not seen in the DeVille
et al. model. The techniques used by DeVille et al. for finding the
mean-field limit are not suitable for this new model. We explore
early attempts at obtaining a new mean-field deterministic system that
would give us an understanding of the behavior seen in the new
model. After redefining the process we do find a mean-field
deterministic system that the model limits on, and we investigate the
behavior of the new model studying the mean-field system.
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