Spiking neural network

Overview

Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential - an intrinsic quality of the neuron related to its membrane electrical charge - reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal.

In the context of spiking neural networks, the current activation level (modeled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and then either firing or decaying over time. Various "coding methods" exist for interpreting the outgoing "spike train" as a real-value number, either relying on the frequency of spikes, or the timing between spikes, to encode information.

Beginnings

The first scientific model of a spiking neuron was proposed by Alan Lloyd Hodgkin and Andrew Huxley in 1952. This model describes how action potentials are initiated and propagated. Spikes, however are not generally transmitted directly between neurons, communication requires the exchange of chemical substances in the synaptic gap, called neurotransmitters. The complexity and variability of biological models have resulted in various neuron models, such as the integrate-and-fire (1907), FitzHugh-Nagumo (1961-1962) and Hindmarsh-Rose model (1984).

From the information theory point of view, the problem is to propose a model that explains how information is encoded and decoded by a series of trains of pulses, i.e., action potentials. Thus, one of the early questions of neuroscience is to determined if neurons communicated by a rate code or by a pulse code. [Maas, W. & Bishop, M.B.(1999) "Pulsed Neural Networks" MIT Press]

Early results with spiking neural models suggested that by using temporal coding, networks of spiking neurons may gain more computational power than traditional neural networks. It was also suggested that, under certain conditions, any multilayered perceptron can be simulated closely by a network consisting of spiking neurons.

References

External links

* Full text of the book [http://icwww.epfl.ch/~gerstner/SPNM/SPNM.html Spiking Neuron Models. Single Neurons, Populations, Plasticity] by Wulfram Gerstner and Werner M. Kistler (ISBN 0521890799)