Learning Spike-Based Population Codes by Reward and Population Feedback

被引：11

作者：

Friedrich, Johannes ^{[1
]}

Urbanczik, Robert ^{[1
]}

Senn, Walter ^{[1
]}

机构：

[1] Univ Bern, Dept Physiol, CH-3012 Bern, Switzerland

来源：

NEURAL COMPUTATION | 2010年 / 22卷 / 07期

基金：

瑞士国家科学基金会;

关键词：

DEPENDENT SYNAPTIC PLASTICITY; REINFORCEMENT; DOPAMINE; REPRESENTATION; NETWORKS; TIME;

D O I：

10.1162/neco.2010.05-09-1010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decisionmaking based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

引用

页码：1698 / 1717

页数：20

共 23 条

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