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Self-Organised Learning in the Chialvo-Bak Model MSc Project

A review of the Chialvo-Bak model is presented, for the two-layer neural network topology. A novel Markov Chain representation is proposed that yields several important analytical quantities and supports a learning convergence argument. The power law regime is re-examined under this new representation and is found to be limited to learning under small mapping changes. A parallel between the power law regime and the biological neural avalanches is proposed. A mechanism to avoid the permanent tagging of synaptic weights of the selective punishment rule is proposed.