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Title: From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation
Authors: Soltoggio, Andrea
Stanley, Kenneth O.
Keywords: Adaptive behavior
Computational models
Neural plasticity
Issue Date: 2012
Publisher: © Elsevier
Citation: SOLTOGGIO, A. and STANLEY, K.O., 2012. From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation. Neural Networks, 34 pp. 28-41.
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.
Description: NOTICE: this is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, vol. 34 (2012). DOI: 10.1016/j.neunet.2012.06.005.
Sponsor: This work was supported by the European Community’s Seventh Framework Programme FP7/2007-2013 Challenge 2 Cognitive Systems, Interaction, Robotics (Grant No. 248311-AMARSi).
Version: Accepted for publication
DOI: 10.1016/j.neunet.2012.06.005
URI: https://dspace.lboro.ac.uk/2134/16988
Publisher Link: http://dx.doi.org/10.1016/j.neunet.2012.06.005
ISSN: 0893-6080
Appears in Collections:Published Articles (Computer Science)

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