Phill Rowcliffe

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In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal networks, both of which are applied to engineering tasks. In exploring the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs, we are able to define(More)
A more plausible biological version of the traditional perceptron is presented here with a learning rule which enables training of the neuron on nonlinear tasks. Three different models are introduced with varying inhibitory and excitatory synaptic connections. Using the derived learning rule, a single neuron is trained to successfully classify the XOR(More)
An algorithm is developed to produce self-organisation of a purely excitatory network of Integrate-and-Fire (IF) neurons, receiving input from a visual scene. The work expands on a clustering algorithm, previously developed for Biological Oscillators, which self-organises similar oscillators into groups and then clusters these groups together. Pixels from(More)
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