A spiking neural model for stable reinforcement of synapses based on multiple distal rewards

Authors:
Michael J. O'Brien;Narayan Srinivasa
Affiliations:
-;-
Venue:
Neural Computation
Year:
2013

Citing 11
Cited 1

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Abstract

In this letter, a novel critic-like algorithm was developed to extend the synaptic plasticity rule described in Florian 2007 and Izhikevich 2007 in order to solve the problem of learning multiple distal rewards simultaneously. The system is augmented with short-term plasticity STP to stabilize the learning dynamics, thereby increasing the system's learning capacity. A theoretical threshold is estimated for the number of distal rewards that this system can learn. The validity of the novel algorithm was verified by computer simulations.