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Connectionist Learning Procedures
Dynamic Connections in Neural Networks
Connectionist Recruitment Learning
4 other sections not shown
actions activity adaptive elements architecture Artificial Neural Networks associative memory assume attribute space backpropagation Barto behavior Boltzmann machine bottom-up boxes system cart-pole clamped committed units competitive learning components concept unit connectionist learning constraints Cybern desired output deterministic distribution dynamic connection dynamic link Edited encode environment equilibrium error propagation expected reinforcement feedback feedforward Feldman Figure free units function global gradient gradient descent Hebbian learning hidden units Hinton Hopfield IEEE implemented input patterns input units input vector input-output intermediate units ISBN layer learning algorithm learning procedure linear machine learning neural networks neurons nodes output units output vectors parallel Parallel Distributed Processing parameter pathway perceptron performance phonemes prediction probability problem recognition recruitment learning reinforcement learning reinforcement signal represent representation Rumelhart sequence simulations stable stochastic structure subnetwork supervised learning supraliminal task theory tion top-down expectation training example update variables weight change