Mathematical Modeling of Neural Information Processing Based on Presynapitc Control and its Applications to Engineering

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This study aims to derive mathematical models of presynaptic control found in the brain, analyse neural information processing mechanisms based on presynaptic control, and view its possible applications to engineering. First, we have proposed a neural network model with breakdown of Dale's principle caused by presynaptic excitatory inhibitory-synapses. In addition, regarding short-term synaptic plasticity, we have estimated parameters such as time constants from physiological data, and proposed a hypothesis on how short-term plasticity modulates corticospinal information flow. Further, we have explored the relation between the presynaptic control and higher brain functions such as attension, memory and excessive synchronization. Finally, on the basis of these results, we have considered possible engineering applications of the related higher brain functions such as memory and attention.