Lecture 16 Spiking neural networks Soft Computing Lecture 16 Spiking neural networks 9.11.2005

Phenomenology of spike generation j threshold -> Spike i Spike reception: EPSP, summation of EPSPs Threshold Spike emission (Action potential) Spike reception: EPSP 9.11.2005

temporal codes The problem of neural coding: Time to first spike after input correlations Phase with respect to oscillation 9.11.2005

Rank Order Coding One possibility takes advantage of the fact that a neuron can be thought of as an analog-delay convertor. It acts somewhat like a capacitance which is progressively charged by an input until it reaches a threshold, at which point it generates an output pulse – the action potential or spike. Such neurons will naturally fire earliest when the input is strong, and will take progressively longer to fire when the input is weaker. In this way, the time at which a neuron fires (its response latency) can be used to code the intensity of the stimulus. However, this sort of code requires knowledge of when the stimulation started, information which is not generally available in the case of the biological visual system. There is, however, a way round this. Consider what happens when several neurons are used in parallel. In this case, even without knowing the precise moment at which the stimulus came on, information can be obtained by looking at the order in which the neurones fire The order of firing of a group of neurons is potentially a very rich source of information about the input pattern 9.11.2005

SpikeNet Is developed for control systems Features of neuron: Feedback from output to inputs for updating of weights At summation of signals take account of frequency of signal Activation function is threshold function Award is used for learning of neuron Inputs is discrete One-layer network from this neurons is able to execute functions which available only for multi layer recurrent networks 9.11.2005

The neural network architecture: SEQAINET (SEQence association and Integration NETwork). 9.11.2005

FPGA – hardware spike neural network for robots 9.11.2005

FPGA – hardware spike neural network for robots (2) 9.11.2005