Delay learning in artificial neural networks

Part 1 Introduction: definitions of learning and reinforcement exploratory learning in a neural network. Part 2 Neural networks and learning with delayed reinforcement: mapping onto pattern association tasks history maintenance prediction-driven reinforcement scope for new models. Part 3 RAM-based nodes and networks: introduction and motivation bit-addressable RAM-nodes the probabilistic logic node Omega-state PLNs and exploratory learning algorithms pRAMs, RAM-nodes and continuous values PLN parameters Iota - the number of inputs to a node Phi-rho - the output probability function omega - the cardinality of the stored value alphabet PLN parameters - conclusions RAM-based nodes - biological connections. Part 4 Attention-driven buffering: the ADB approach an example the location of the buffer an example "bug" comparison with other delay learning methods. Part 5 Analysis of parameters: reinforcement delay and buffer size training set and learning rule learning system topology construction of nodes generalization tests attention-driven buffering - conclusions. Part 6 An ADB system for operant conditioning: the operant conditioning task description of the simulation the unspecific effect operant conditioning experiments with OVSIM simple discrimination tasks delay to attack stimuli task interference and relearning transfer of discrimination learning tasks involving multiple discrimination OVSIM as an operant conditioning model. Part 7 OVSIM and the octopus - the case for modelling: an overview of the octopus visual learning system OVSIM as a model of the octopus visual attack learning system damage learning experiments another model of the octopus visual learning system. Part 8 ADB and delay learning in higher animals: physiological relevance of ADB objectives possible methods possible mechanisms for ADB.