Title :
Reward-modulated synaptic plasticity for simple Bayesian decision
Author_Institution :
Fac. of Inst. of Phys. Health & Psychol., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
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 winner-take-all neural circuit can model the mainly experiment results by Yang and Shadlen.
Keywords :
Bayes methods; inference mechanisms; neural nets; neurophysiology; Bayesian inference; learning rule; logLR; logarithm likelihood ratio; nervous system; neural currency; reward-modulated synaptic plasticity; reward-modulated synaptic rule; simple Bayesian decision; winner-take-all neural circuit; Bayesian methods; Decision making; Integrated circuit modeling; Markov processes; Noise; Shape; Bayesian inference; Decision making; Learning rule; Neural network;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968893
