Learning spike-based population codes by reward and population feedback

Authors:
Johannes Friedrich;Robert Urbanczik;Walter Senn
Affiliations:
-;-;-
Venue:
Neural Computation
Year:
2010

Citing 4
Cited 0

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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 -ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.