Computational models of cognition
Doris, Thomas Francis (2005) Computational models of cognition. PhD thesis, Dublin City University.
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Existing connectionist computational models of neural networks idealise the biological process in the neuron to a discrete summation, and fail to provide an efficient substrate for computation involving the spectral data that is the input to the biological perceptual process. This work presents a computational model of neural function that introduces a continuous analogue process and explores the computational uses of sub-threshold oscillations of the membrane potential. The goal of tins work is to present an in itial examination of the advantages to the practitioner that are afforded by a new computational model of the neuron that includes sub-threshold oscillations as a component on an equal footing with axonal impulses themselves. The relevant. evidence that these effects are important in a biological neural network is presented. The new resonate-and-fire model is presented and mathematically defined, and shown to be a superset of the ubiquitous integrate-and-fire model. The behaviour patterns of the model are explored initially in single neurons and then networks are examined and shown to be capable of exhibiting useful excitation patterns such as tonic oscillation, selective innervation and resonance. An unsupervised learning algorithm is defined and shown to generate networks that naturally organise to perform Fourier-style transforms central to spectral manipulations. Finally, the model is examined with respect to the current theories of computational neuroscience and cognitive science, and its p otential uses in these domains described.
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