Spiking neural nets with symbolic internal state

Authors:
Carl O'Dwyer;Daniel Richardson
Affiliations:
Computer Science, Bath University, Bath BA2 7AY, UK;Computer Science, Bath University, Bath BA2 7AY, UK
Venue:
Information Processing Letters - Special issue on applications of spiking neural networks
Year:
2005

Citing 6
Cited 0

Spiking Neuron Models: An Introduction

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Real-time computing without stable states: a new framework for neural computation based on perturbations

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Event-driven simulation of spiking neurons with stochastic dynamics

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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

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Lower bounds for the computational power of networks of spiking neurons

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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.