Studies of Mind and Brain: Neural Principles of Learning, by Stephen T. Grossberg

By Stephen T. Grossberg

the mass of experimental info from present study in psychology and body structure, Grossberg proposes and develops a non-linear arithmetic as a version for particular features of brain and mind. He unearths the vintage method of the mathematical modelling of brain and mind systematically insufficient. This inadequacy, he holds, arises from the try to describe adaptive platforms within the mathematical language of nine physics constructed to explain "stationary", i. e. non-adaptive and non-evolving platforms. instead of this linear arithmetic, Grossberg develops his non-linear method. His process is right away creative, rigorous, and philosophically major: it's the proposal scan. it's right here that the richness of his interdisciplinary mastery, and the facility of his equipment, buildings and proofs, display themselves. the strategy is what C. S. Peirce characterised because the approach to abduction, or of hypothetical inference in conception development: given the output of the method as a mental phenomenon (e. g.

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Extra info for Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control

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_ \ I I An important property of a dipole field is , I I I \ I I I this: If a test input excites a particular on-cell, 11\'1 / 1 1 II /11 then the on-cell inhibits its off-cell. The \ " '--"" / I inhibited off-cell can, in turn, disinhibit a ..... .... ",/ ...... nearby off-cell due to the tonic arousal input Ie) and the recurrent anatomy within the off-cell field. The disinhibited off-cell thereupon Figure 15. ), a. dial symmetry is inhibits its on-cell via dipole interactions. inspected for a.

And F(2) elicit interfield signals that mutually reinforce each other, and activities at both levels are amplified and locked into STM. Because the STM activities can now persist much longer in time than the passive decay rates of individual cells, the slowly varying feed-forward filters and feedback expectancies have sufficient time to sample the STM patterns and store them in LTM. I call this dynamical state an adaptive resonance. The resonant state provides a global in terpreta tion of the afferent data, or a context-dependent code, that explicates in neural terms the idea that the network is paying attention to the data.

Of special importance is the observation that unless resonance occurs, no coding in L TM can take place. This observation clarifies from a mechanistic viewpoint the psychological fact that a relationship exists between paying attention to an event and coding it in LTM (Craik & Lockhart, 1972; Craik & Tulving, 1975). The resonant state provides a contextdependen t code due to several factors acting together. For example, the pattern of expectancy feedback can alter, through a matching process, the activity that a given.

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