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On the Realization of a Kolmogorov Network
Acetylcholine and Learning in a Cortical Associative Memory
Convergent Algorithm for Sensory Receptive Field Development
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1993 Massachusetts Institute activation adaptive adaptive filtering approximation architecture associative memory attractors backpropagation behavior cells killed coefficients Comp competitive complexity connections constraints convergence correlations corresponding cortex cortical cost function data clustering data points defined denotes digits disorder distribution dynamics entropy equation error estimation example feature feature extraction feedback feedforward filter gaussian gradient hint Hinton Hopfield IEEE implementation input pattern Institute of Technology interneurons interpolation learning algorithm learning rule linear maps masking matrix method network model Neural Computation neural network neurons node noise nonlinear number of clusters optic flow optimal oscillator output parameters performance phase pixels predicted problem projection pursuit receptive fields recognition response Rumelhart sequence shown in Figure sigmoid function signal simulations solution spike statistical synaptic Theorem tion University unsupervised learning values variables VC dimension VC(G vector vector quantization visual visual cortex weights zero