A gradient learning rule for the tempotron

Authors:
Robert Urbanczik;Walter Senn
Affiliations:
-;-
Venue:
Neural Computation
Year:
2009

Citing 2
Cited 0

Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning

Neural Computation
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity

Neural Computation

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Abstract

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.