Temporal dynamics of rate-based synaptic plasticity rules in a stochastic model of spike-timing-dependent plasticity

Temporal dynamics of rate-based synaptic plasticity rules in a stochastic model of spike-timing-dependent plasticity

In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, Delta S-n, induced by a typical sequence of precisely n spikes. We found that the rules Delta S-n, n >= 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules Delta S-n, however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strength are conducted over definite periods of time during which a potentially unknown number of spikes may occur. Here, therefore, we derive an expression, Delta S(t), for the expected change in synaptic strength of a synapse experiencing an average sequence of spikes of typical length occurring during a fixed period of time, t. We find that the resulting synaptic plasticity rule Delta S(t) exhibits a number of remarkable properties. It is an entirely self-stabilizing learning rule in all regions of parameter space. Further, its parameter space is carved up into three distinct, contiguous regions in which the exhibited synaptic interactions undergo different transitions as the time t is increased. In one region, the synaptic dynamics change from noncompetitive to competitive to entirely depressing. In a second region, the dynamics change from noncompetitive to competitive without the second transition to entirely depressing dynamics. In a third region, the dynamics are always noncompetitive. The locations of these regions are not fixed in parameter space but may be modified by changing the mean presynaptic firing rates. Thus, neurons may be moved among these three different regions and so exhibit different sets of synaptic dynamics depending on their mean firing rates.

2253-2307

Elliott, T

b4262f0d-c295-4ea4-b5d8-3931470952f9

2008

Elliott, T

b4262f0d-c295-4ea4-b5d8-3931470952f9

Elliott, T
(2008)
Temporal dynamics of rate-based synaptic plasticity rules in a stochastic model of spike-timing-dependent plasticity.
*Neural Computation*, 20, .

## Abstract

In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, Delta S-n, induced by a typical sequence of precisely n spikes. We found that the rules Delta S-n, n >= 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules Delta S-n, however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strength are conducted over definite periods of time during which a potentially unknown number of spikes may occur. Here, therefore, we derive an expression, Delta S(t), for the expected change in synaptic strength of a synapse experiencing an average sequence of spikes of typical length occurring during a fixed period of time, t. We find that the resulting synaptic plasticity rule Delta S(t) exhibits a number of remarkable properties. It is an entirely self-stabilizing learning rule in all regions of parameter space. Further, its parameter space is carved up into three distinct, contiguous regions in which the exhibited synaptic interactions undergo different transitions as the time t is increased. In one region, the synaptic dynamics change from noncompetitive to competitive to entirely depressing. In a second region, the dynamics change from noncompetitive to competitive without the second transition to entirely depressing dynamics. In a third region, the dynamics are always noncompetitive. The locations of these regions are not fixed in parameter space but may be modified by changing the mean presynaptic firing rates. Thus, neurons may be moved among these three different regions and so exhibit different sets of synaptic dynamics depending on their mean firing rates.

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## More information

Published date: 2008

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Imported from ISI Web of Science

Organisations:
Web & Internet Science

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Local EPrints ID: 269345

URI: http://eprints.soton.ac.uk/id/eprint/269345

PURE UUID: fbdc2038-46fb-45ed-9e17-86fd68a5ef3a

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Date deposited: 21 Apr 2010 07:46

Last modified: 16 Jul 2019 22:21

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## Contributors

Author:
T Elliott

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