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.
Department of Physiology, University of Bern, CH-3012 Bern, Switzerland. friedrich@pyl.unibe.ch
Johannes
J
Friedrich
Robert
R
Urbanczik
Walter
W
Senn
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