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Imperial
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Abstract
This investigation, of multidisciplinary interest, delves into how the structure and connectivity of the brain influences neural processing. This thesis addresses issues of modelling brain function and consequent changes due to neural disorders. It is carried out through computer simulations within a systemic framework. The work offers plausible explanations for selected neural processing by bringing together topics from neuroscience, medical imaging, artificial intelligence, and computing. In doing so, it also helps to reduce the gap between neurobiology and cognitive neuroscience. All of the above topics are presented in a coordinated manner, gradually transitioning from biological to artificial “worlds”. The employed formalism and holistic approach are aimed at systemic analyses of granularities compatible with data acquisition techniques.
The Venn-network, a novel neural network model proposed by the author, is introduced and exhaustively simulated. This new neural network architecture allows definition and use of multiple types of processing unit, multiple regions within the network structure, and several types of axonal fibres. Most interestingly, the internal activity of utilised networks resembles images produced by functional brain imaging.
A comprehensive computer simulator was developed for implementing the Venn-network model. This simulator was used to carry out experiments such as structural-functional equivalence, active-passive activations, modulation, ageing, and contra-lateral inhibition. Next, disrupting effects such as the ones produced by (1) multiple sclerosis plaques and (2) strokes, were applied in these simulations. Throughout these simulations, Venn-networks were trained to control flexions of (ten) virtual fingers to reproduce movements of a piano player performing a Mozart Sonata.
The Venn-network model proved to be an effective tool for predicting behaviour in both physiological and pathological scenarios. The correctness and robustness of all implemented Venn-networks was verified in the simulations carried out, as the functionality of all neural architectures conformed with the expected behaviour.
Potential applications of the research include: (i) to support prognoses in neurology (e.g. multiple sclerosis effects due to plaques growth and inference of damage due to strokes), (ii) as a test-bed for producing insights into neuromorphic systems, (iii) to interface between cognitive algorithms and front-end electronics of robots, (iv) as a brain interface for controlling prosthetics, and (v) to provide underlying models for checking hypotheses in medical imaging experiments.