Learning spike-based population codes by reward and population feedback.
- Friedrich J Department of Physiology, University of Bern, CH-3012 Bern, Switzerland. friedrich@pyl.unibe.ch
- Urbanczik R
- Senn W
- 2010-03-19
Published in:
- Neural computation. - 2010
Action Potentials
Animals
Artificial Intelligence
Decision Making
Feedback, Physiological
Humans
Learning
Models, Neurological
Nerve Net
Neural Networks, Computer
Neurons
Reaction Time
Reward
English
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.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1162/neco.2010.05-09-1010
- PMID 20235820
- Persistent URL
- https://sonar.ch/global/documents/163
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