A Globally Asymptotically Stable Plasticity Rule for Firing Rate Homeostasis

Authors:
Prashant Joshi;Jochen Triesch
Affiliations:
Computation and Neural Systems Program, Division of Biology, 216-76, California Institute of Technology, Pasadena, USA CA 91125 and Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe ...;Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany 60438
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
A gradient rule for the plasticity of a neuron’s intrinsic excitability

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

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Abstract

How can neural circuits maintain stable activity states when they are constantly being modified by Hebbian processes that are notorious for being unstable? A new synaptic plasticity mechanism is presented here that enables a neuron to obtain homeostasis of its firing rate over longer timescales while leaving the neuron free to exhibit fluctuating dynamics in response to external inputs. Mathematical results demonstrate that this rule is globally asymptotically stable. Performance of the rule is benchmarked through simulations from single neuron to network level, using sigmoidal neurons as well as spiking neurons with dynamic synapses.