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Learning spike-based population codes by reward and population feedback
Johannes Friedrich1, Robert Urbanczik, Walter Senn
1Department of Physiology, University of Bern, CH-3012 Bern, Switzerland. friedrich@pyl.unibe.ch
Abstract:
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.
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