A Modified Spiking Neuron that Involves Derivative of the State Function at Firing Time

Authors:
Wenyu Yang;Jie Yang;Wei Wu
Affiliations:
School of Mathematical Sciences, Dalian University of Technology, Dalian, China;School of Mathematical Sciences, Dalian University of Technology, Dalian, China;School of Mathematical Sciences, Dalian University of Technology, Dalian, China
Venue:
Neural Processing Letters
Year:
2012

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Abstract

In usual spiking neural networks, the real world information is interpreted as spike time. A spiking neuron of the spiking neural network receives input vector of spike times, and activates a state function x(t) by increasing the time t until the value of x(t) reaches certain threshold value at a firing time t a . And t a is the output of the spiking neuron. In this paper we propose, and investigate the performance of, a modified spiking neuron, of which the output is a linear combination of the firing time t a and the derivative x驴(t a ). The merit of the modified spiking neuron is shown by numerical experiments for solving some benchmark problems: The computational time of a modified spiking neuron is a little greater than that of a usual spiking neuron, but the accuracy of a modified spiking neuron is almost as good as a usual spiking neural network with a hidden layer.