A gradient learning rule for the tempotron.
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.
Department of Physiology, University of Bern, 3012-Bern, Switzerland. urbanczik@pyl.unibe.ch
Robert
R
Urbanczik
Walter
W
Senn
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013997:Time Factors