We studied the dynamics of a neural network which have both of recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak trans...
In this paper we demonstrate the generalization property of spiking neurons trained with ReSuMe method. We show in a set of experiments that the learning neuron can approximate the...
We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs using a piecewise linear approximation of the Quadratic Integrate...
Hooman Shayani, Peter J. Bentley, Andrew M. Tyrrel...
Different credit assignment strategies are investigated in a two level co-evolutionary model which involves a population of Gaussian neurons and a population of radial basis funct...
Abstract. Invertebrate nervous systems serve as important models for neuroscience research because they are comprised of relatively small numbers of individually identified neurons...
Jason A. Pamplin, Ying Zhu, Paul S. Katz, Rajshekh...
— The present paper is devoted to the exploration of the properties of the simple spiking neuron model and quantification of the information transfer rate, which the separate neu...
Neurotransmitter fields differ from neural fields in the underlying principle that the state variables are not the neuron action potentials, but the chemical concentration of neuro...
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially ...