Abstract
Introduction It has been shown that a form of temporal difference (TD) learning for predicting the value of the membrane potential of a neuron, at a fixed delay after the neuron received a presynaptic spike, results in a learning rule that is very similar to hebbian spike-timing-dependent plasticity (STDP) [1]. Since this result was obtained using a relatively complex neural model (two-compartmental, with Hodgkin-Huxley-like currents) and a simple setup (a single presynaptic spike followed by a single current pulse), we investigated whether it holds for simpler neural models and for more general situations. This is relevant both for the theoretical understanding of this phenomenon and for verifying that it holds in common simulations of spiking neural networks.
Highlights
It has been shown that a form of temporal difference (TD) learning for predicting the value of the membrane potential of a neuron, at a fixed delay after the neuron received a presynaptic spike, results in a learning rule that is very similar to hebbian spike-timing-dependent plasticity (STDP) [1]
For the IAF neuron, we found a hebbian, STDP-like plasticity rule only when postsynaptic spikes were generated by a single current pulse and the reset potential of the neuron was positive
There is a qualitative difference with respect to the IAF case because the Izhikevich neuron incorporates the dynamics of the membrane potential during the onset of the action potential
Summary
It has been shown that a form of temporal difference (TD) learning for predicting the value of the membrane potential of a neuron, at a fixed delay after the neuron received a presynaptic spike, results in a learning rule that is very similar to hebbian spike-timing-dependent plasticity (STDP) [1]. Email: Cătălin V Rusu* - rusu@coneural.org * Corresponding author from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. Published: 13 July 2009 BMC Neuroscience 2009, 10(Suppl 1):P201 doi:10.1186/1471-2202-10-S1-P201
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