Bumps, breathers, and waves in a neural network with spike frequency adaptation

In this Letter we introduce a continuum model of neural tissue that include the effects of so-called spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon the non-local network connectivity, synaptic response, and firing rate of a single n...

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Main Authors: Coombes, Stephen, Owen, Markus R.
Format: Article
Published: American Physical Society 2005
Online Access:https://eprints.nottingham.ac.uk/149/
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author Coombes, Stephen
Owen, Markus R.
author_facet Coombes, Stephen
Owen, Markus R.
author_sort Coombes, Stephen
building Nottingham Research Data Repository
collection Online Access
description In this Letter we introduce a continuum model of neural tissue that include the effects of so-called spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon the non-local network connectivity, synaptic response, and firing rate of a single neuron. A phenomenological model of SFA is examined whereby the firing rate is taken to be a simple state-dependent threshold function. As in the case without SFA classical Mexican-Hat connectivity is shown to allow for the existence of spatially localized states (bumps). Importantly an analysis of bump stability using recent Evans function techniques shows that bumps may undergo instabilities leading to the emergence of both breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. Direct numerical simulations both confirm our theoretical predictions and illustrate the rich dynamic behavior of this model, including the appearance of self-replicating bumps.
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spelling nottingham-1492020-05-04T16:26:04Z https://eprints.nottingham.ac.uk/149/ Bumps, breathers, and waves in a neural network with spike frequency adaptation Coombes, Stephen Owen, Markus R. In this Letter we introduce a continuum model of neural tissue that include the effects of so-called spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon the non-local network connectivity, synaptic response, and firing rate of a single neuron. A phenomenological model of SFA is examined whereby the firing rate is taken to be a simple state-dependent threshold function. As in the case without SFA classical Mexican-Hat connectivity is shown to allow for the existence of spatially localized states (bumps). Importantly an analysis of bump stability using recent Evans function techniques shows that bumps may undergo instabilities leading to the emergence of both breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. Direct numerical simulations both confirm our theoretical predictions and illustrate the rich dynamic behavior of this model, including the appearance of self-replicating bumps. American Physical Society 2005-04-15 Article PeerReviewed Coombes, Stephen and Owen, Markus R. (2005) Bumps, breathers, and waves in a neural network with spike frequency adaptation. Physical Review Letters, 94 (14). pp. 148102-1. ISSN 1079-7114 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.94.148102 https://doi.org/10.1103/PhysRevLett.94.148102 https://doi.org/10.1103/PhysRevLett.94.148102
spellingShingle Coombes, Stephen
Owen, Markus R.
Bumps, breathers, and waves in a neural network with spike frequency adaptation
title Bumps, breathers, and waves in a neural network with spike frequency adaptation
title_full Bumps, breathers, and waves in a neural network with spike frequency adaptation
title_fullStr Bumps, breathers, and waves in a neural network with spike frequency adaptation
title_full_unstemmed Bumps, breathers, and waves in a neural network with spike frequency adaptation
title_short Bumps, breathers, and waves in a neural network with spike frequency adaptation
title_sort bumps, breathers, and waves in a neural network with spike frequency adaptation
url https://eprints.nottingham.ac.uk/149/
https://eprints.nottingham.ac.uk/149/
https://eprints.nottingham.ac.uk/149/