From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation

Andrea Soltoggio, Kenneth O. Stanley
2012 Neural Networks  
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
more » ... 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.
doi:10.1016/j.neunet.2012.06.005 pmid:22796669 fatcat:zuddmbqbtzf5lgxbl6dtc2lizy