Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Controlthe mass of experimental data from current research in psychology and physiology, Grossberg proposes and develops a non-linear mathematics as a model for specific functions of mind and brain. He finds the classic approach to the mathematical modelling of mind and brain systematically inadequate. This inadequacy, he holds, arises from the attempt to describe adaptive systems in the mathematical language of 9 physics developed to describe "stationary", i. e. non-adaptive and non-evolving systems. In place of this linear mathematics, Grossberg develops his non-linear approach. His method is at once imaginative, rigorous, and philosophically significant: it is the thought experiment. It is here that the richness of his interdisciplinary mastery, and the power of his methods, constructions and proofs, reveal themselves. The method is what C. S. Peirce characterized as the method of abduction, or of hypothetical inference in theory construction: given the output of the system as a psychological phenomenon (e. g. |
Contents
| 1 | |
Some Physiological and Biochemical Consequences of Psycho | 53 |
Classical and Instrumental Learning by Neural Networks | 65 |
Pattern Learning by FunctionalDifferential Neural Networks with | 157 |
Quantitative | 194 |
A Neural Model of Attention Reinforcement and Discrimination | 229 |
Cerebellar and Retinal Analogs of Cells Fired | 296 |
Contour Enhancement Short Term Memory and Constancies | 332 |
Decision Rules Pattern Formation | 379 |
Competition Decision and Consensus | 399 |
Serial Binary Mem | 425 |
Paral | 448 |
SelfOrganization and Performance | 498 |
LIST OF PUBLICATIONS | 640 |
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Studies of Mind and Brain: Neural Principles of Learning, Perception ... S.T. Grossberg No preview available - 1982 |
Common terms and phrases
adaptive adaptive coding amacrine cells analogous anatomy arousal level axon behavior buffer cells channels chunks classical conditioning competitive conditioning contrast cues dipole drive elicit equations example excitatory excited exists feature detectors feedback inhibition feedback signals field filtering function given global gradient Grossberg heterarchy incentive motivation increasing inhibition inhibitory input pattern interactions interneurons lateral inhibition LTM traces mathematical mechanism memory monotone decreasing motor neural networks noise nonlinear nonspecific arousal normalization occurs off-cell on-cell on-center off-surround order information output outstar parameters pathways performance populations positive postulates primacy effect properties proprioceptive quenching threshold rebound recency effect recurrent rehearsal representations reset resonance response reverberation sampling sampling signals Section sensory sequence serial learning shock short-term memory shunting sigmoid function spatial pattern STM activity STM pattern Suppose synaptic knobs terminal map Theorem theory tion transmitter trials v₁ V₂ variables visual x₁