Title

From Modulated Hebbian Plasticity To Simple Behavior Learning Through Noise And Weight Saturation

Keywords

Adaptive behavior; Computational models; Learning; Neural plasticity; Neuromodulation

Abstract

Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation. © 2012 Elsevier Ltd.

Publication Date

10-1-2012

Publication Title

Neural Networks

Volume

34

Number of Pages

28-41

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.neunet.2012.06.005

Socpus ID

84865440918 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84865440918

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