Investigation of generalization ability of a trained stochastic kinetic model of neuron

摘要

In this work we focus on the generalization ability of a biological neuron model. We consider a Hodgkin–Huxley type of biological neuron model, based on Markov kinetic schemes, trained with the gradient descent algorithm.The examination of the generalization ability of the kinetic model of neuron is performed with methods derived from the regularization theory. The error function of the neuron model is supplemented with a regularizer. We examine two different forms of the regularizer: a penalty function, which is a sum of squared weights of the neuron model, and a Tikhonov functional, which is a linear differential operator related to the input–output mapping.As an example we consider a stochastic kinetic model of neuron to solve a problem of noise reduction in an image.Additionally in the paper we present different measures to show that adding a regularizer to the error function does not worsen the obtained results of noise reduction in an image.