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Robert Urbanczik
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2010) 22 (7): 1698–1717.
Published: 01 July 2010
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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.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2009) 21 (2): 340–352.
Published: 01 February 2009
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We introduce a new supervised learning rule for the tempotron task: the binary classification of input spike trains by an integrate-and-fire neuron that encodes its decision by firing or not firing. The rule is based on the gradient of a cost function, is found to have enhanced performance, and does not rely on a specific reset mechanism in the integrate-and-fire neuron.