Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
Abstract
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing dependent plas- ticity (STDP) function which depends on the time course of excitatory postsynaptic potentials (EPSPs) and the autocorrelation function of the postsynaptic neuron. We show that the STDP function has both positive and negative phases. The positive phase is related to the shape of the EPSP while the negative phase is controlled by neuronal refractoriness.
Cite
Text
Toyoizumi et al. "Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model." Neural Information Processing Systems, 2004.Markdown
[Toyoizumi et al. "Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/toyoizumi2004neurips-spiketiming/)BibTeX
@inproceedings{toyoizumi2004neurips-spiketiming,
title = {{Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model}},
author = {Toyoizumi, Taro and Pfister, Jean-pascal and Aihara, Kazuyuki and Gerstner, Wulfram},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1409-1416},
url = {https://mlanthology.org/neurips/2004/toyoizumi2004neurips-spiketiming/}
}