Spiking neural nets with symbolic internal state

Authors:
Carl O'Dwyer;Daniel Richardson
Affiliations:
Computer Science, Bath University, Bath, UK;Computer Science, Bath University, Bath, UK
Venue:
Information Processing Letters - Special issue on applications of spiking neural networks
Year:
2005

Citing 6
Cited 0

Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Real-time computing without stable states: a new framework for neural computation based on perturbations

Neural Computation
Event-driven simulation of spiking neurons with stochastic dynamics

Neural Computation
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)

Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

Neural Computation
Lower bounds for the computational power of networks of spiking neurons

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A type of spiking neural net in which the internal state functions of the neurons are represented symbolically rather than numerically is described. This makes possible subtle forms of short term memory, but also raises both computational and mathematical problems. It is shown how interval arithmetic can be used for event driven simulation. Computational times are given for some tests of an implementation resulting from these ideas. Applications are briefly discussed.