title: A spiking neural model for stable reinforcement of synapses based on multiple distal rewards.
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.
authors.affiliation: Department of Mathematics, University of California at Los Angeles, Los Angeles, CA 90095, USA. mobrien@math.ucla.edu
authors.forename: Michael J
authors.initials: MJ
authors.lastname: O'Brien
authors.forename: Narayan
authors.initials: N
authors.lastname: Srinivasa
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003216:Conditioning, Operant / Q000502:physiology; D003463:Cues; D005246:Feedback; D006801:Humans; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D011337:Probability Learning; D012054:Reinforcement, Psychology; D012201:Reward; D013569:Synapses / Q000502:physiology*