An Adaptive Neural Network: The Cerebral Cortex |
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Page 55
... depends upon response probability , and learning can become impossible if the response demanded is too unusual for the animal . - Learning of a new movement ( operant conditioning ) depends upon that movement's " proximity " to a ...
... depends upon response probability , and learning can become impossible if the response demanded is too unusual for the animal . - Learning of a new movement ( operant conditioning ) depends upon that movement's " proximity " to a ...
Page 309
... depends upon a prior combination with a transmission coefficient specific to each receptive zone [ a ] or [ b ] . Neuronal integration of two inputs and can be schematically illustrated by an " equivalent logical circuit " ( Figure A ...
... depends upon a prior combination with a transmission coefficient specific to each receptive zone [ a ] or [ b ] . Neuronal integration of two inputs and can be schematically illustrated by an " equivalent logical circuit " ( Figure A ...
Page 334
... depends upon the relative extension EX ( k ) / EX ( 0 ) = 1/2 to the ( k ) th power . The greater the number of modules called , the weaker the call coefficient . The call tends to diminish as the goal becomes distant . For an ...
... depends upon the relative extension EX ( k ) / EX ( 0 ) = 1/2 to the ( k ) th power . The greater the number of modules called , the weaker the call coefficient . The call tends to diminish as the goal becomes distant . For an ...
Contents
Introduction | 1 |
basic function of neurons | 23 |
From neuronal to tissular function | 40 |
Copyright | |
27 other sections not shown
Common terms and phrases
action potential activity adaptations afferent associative areas auditory automaton axes axons behavioral call trees cellular cerebellum cerebral cortex chapter circuits columnar automata combinations construction correspond cortical actions cortical areas cortical column cortical images cortical inputs cortical modules cortical network cortical regions cortical surface cortical system cortical zone coupling dendrites depends differentiation effect example excitatory external Figure frontal areas frontal cortex frontal regions fundamental programs global hippocampus inhibition inhibitory internal interneurons lateral inhibition learning level E2 linked lower layers maps membrane memorization modular actions module molecular motor areas neural structures organization orientation output parallel parameters partial goals pathways pattern phonemes position produce properties Purkinje cells pyramidal cells pyramidal neurons receptor recognition algorithms relations represent reticular reticular formation retinal sensorimotor sensory sequence somatosensory spatial specific symbolic symmetrical synapses temporal levels thalamic inputs tissue transmission coefficients transmitter triggered uncoupling upper and lower upper layers upper pyramidal cells visual words
References to this book
Artificial Life and Virtual Reality Nadia Magnenat-Thalmann,Daniel Thalmann No preview available - 1994 |