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A biologically inspired model of decision making controlled by random neural activity.

This work describes research in detailed neural modelling of decision making. Although the study of formal models and of the psychology of decision making has a long history, only relatively recently has it been possible to explore its neural basis. Our models and simulations are based on a simple benchmark visual decision task. This is a typical task that has been used for decades in neurological studies in which a primate has to express with an eye movement a decision based on simple visual stimuli called kinematograms, whilst its neural activity is recorded. This experimental paradigm has enabled the discovery of neural pathways and patterns of activity involved in decision making. However, the neural mechanisms generating these features are still a subject of intensive investigation. In this thesis, after reviewing the experimental background, we give an overview of current neural modelling of decision making. We then propose a more detailed and biologically inspired neural network model that generates such behaviour, from stimulus perception to decision making and the eye movement that expresses it. First, we model early visual processing and discrimination of the general direction of movement in the stimulus. This model explains how correlation and inhibitory inputs account for the input variance to integrate and fire neurons and shows that increasing this variance is in fact beneficial to the discrimination accuracy of the neuron. Then we present a model for decision making based on accumulation of evidence and neural competition generated by recurrent excitation and inhibition. Our simulations of this model show that the low level stochastic background activity of the brain can control the speed and accuracy of decision making. The underlying conviction of this work is that random neural activity is fundamental to understanding the dynamics of most stages of any mental activity. Submitted for the degree of Doctor of Philosophy University of Sussex