Learning spike-based population codes by reward and population feedback.
We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.
Friedrich
Johannes
J
Department of Physiology, University of Bern, CH-3012 Bern, Switzerland. friedrich@pyl.unibe.ch
Urbanczik
Robert
R
Senn
Walter
W
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D003657:Decision Making / Q000502:physiology; D025461:Feedback, Physiological / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D012201:Reward