Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
Abstract
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally relevant adaptive changes in com- plex networks of spiking neurons. However the potential and limitations of this learning rule could so far only be tested through computer simulations. This ar- ticle provides tools for an analytic treatment of reward-modulated STDP, which allow us to predict under which conditions reward-modulated STDP will be able to achieve a desired learning effect. In particular, we can produce in this way a theoretical explanation and a computer model for a fundamental experimental finding on biofeedback in monkeys (reported in [1]).
Cite
Text
Pecevski et al. "Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity." Neural Information Processing Systems, 2007.Markdown
[Pecevski et al. "Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/pecevski2007neurips-theoretical/)BibTeX
@inproceedings{pecevski2007neurips-theoretical,
title = {{Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity}},
author = {Pecevski, Dejan and Maass, Wolfgang and Legenstein, Robert A.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {881-888},
url = {https://mlanthology.org/neurips/2007/pecevski2007neurips-theoretical/}
}