Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission

by: Sebastian H Seung

Neuron, Vol. 40, No. 6. (18 December 2003), pp. 1063-1073.

View FullText article

DOI, ScienceDirect

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

edauce, awooga, mbregman

Find related articles with these CiteULike tags

dopamine, feedback, hebb, hedonistic-neuron, learning, no-tag, reinforcement, reinforcement-learning, xor

Abstract

It is well-known that chemical synaptic transmission is an unreliable process, but the function of such unreliability remains unclear. Here I consider the hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution. This is possible if synapses are "hedonistic," responding to a global reward signal by increasing their probabilities of vesicle release or failure, depending on which action immediately preceded reward. Hedonistic synapses learn by computing a stochastic approximation to the gradient of the average reward. They are compatible with synaptic dynamics such as short-term facilitation and depression and with the intricacies of dendritic integration and action potential generation. A network of hedonistic synapses can be trained to perform a desired computation by administering reward appropriately, as illustrated here through numerical simulations of integrate-and-fire model neurons.

@article{citeulike:884653, abstract = {It is well-known that chemical synaptic transmission is an unreliable process, but the function of such unreliability remains unclear. Here I consider the hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution. This is possible if synapses are "hedonistic," responding to a global reward signal by increasing their probabilities of vesicle release or failure, depending on which action immediately preceded reward. Hedonistic synapses learn by computing a stochastic approximation to the gradient of the average reward. They are compatible with synaptic dynamics such as short-term facilitation and depression and with the intricacies of dendritic integration and action potential generation. A network of hedonistic synapses can be trained to perform a desired computation by administering reward appropriately, as illustrated here through numerical simulations of integrate-and-fire model neurons.}, author = {Seung, Sebastian H. }, citeulike-article-id = {884653}, doi = {10.1016/S0896-6273(03)00761-X}, journal = {Neuron}, month = {December}, number = {6}, pages = {1063--1073}, posted-at = {2008-02-28 18:36:58}, priority = {2}, title = {Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission}, url = {http://dx.doi.org/10.1016/S0896-6273(03)00761-X}, volume = {40}, year = {2003} }

RIS record

TY - JOUR ID - citeulike:884653 L3 - citeulike-article-id:884653 TI - Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission JF - Neuron VL - 40 IS - 6 SP - 1063 EP - 1073 N2 - It is well-known that chemical synaptic transmission is an unreliable process, but the function of such unreliability remains unclear. Here I consider the hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution. This is possible if synapses are "hedonistic," responding to a global reward signal by increasing their probabilities of vesicle release or failure, depending on which action immediately preceded reward. Hedonistic synapses learn by computing a stochastic approximation to the gradient of the average reward. They are compatible with synaptic dynamics such as short-term facilitation and depression and with the intricacies of dendritic integration and action potential generation. A network of hedonistic synapses can be trained to perform a desired computation by administering reward appropriately, as illustrated here through numerical simulations of integrate-and-fire model neurons. AU - Seung, Sebastian PY - 2003/12/18/ UR - http://dx.doi.org/10.1016/S0896-6273(03)00761-X ER -

RIS BibTeX