Reward-Modulated Synaptic Plasticity for Simple Bayesian Decision

摘要：

Gold and Shadlen have proposed that a simple quantity, logarithm likelihood ratio(logLR), can provide a neural currency for Bayesian inference of two alternative choice tasks. However, it remains unclear how our nervous system could acquire the capability to carry out this computation. In this paper we propose a learning rule operating on logLR. In particular we show that the experimentally supported type of reward-modulated synaptic rule in combination with a winnertake all neural circuit can model the mainly experiment results by Yang and Shadlen.

关键词： Decision making Bayesian inference Neural network Learning rule

作者: Wang Yuxiu

作者单位: Faculty of Institute of Physical Health and Psychology,Zhejiang University of Technology,Hangzhou,310014,China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3854-3859

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）