7 research outputs found
Computational modeling with spiking neural networks
This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed
A discrete time neural network model with spiking neurons II. Dynamics with noise
We provide rigorous and exact results characterizing the statistics of spike
trains in a network of leaky integrate and fire neurons, where time is discrete
and where neurons are submitted to noise, without restriction on the synaptic
weights. We show the existence and uniqueness of an invariant measure of Gibbs
type and discuss its properties. We also discuss Markovian approximations and
relate them to the approaches currently used in computational neuroscience to
analyse experimental spike trains statistics.Comment: 43 pages - revised version - to appear il Journal of Mathematical
Biolog