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Author(s): Grossberg, S. |
Year: 1969
Citation: Studies in Applied Mathematics, 48, 105-132
Abstract: Many of our sensory and motor organs have linearly ordered components, for example the fingers on a hand, the tonotopic organization of the auditory system, the successivjeo ints on arms and legs,t he spine,e tc. This paper beginsa discussion of some nonlinear networks which can learn complicated spatiotemporal patterns among sensory and motor organs with linearly ordered components. These networks will ultimately resemblec erebrocerebellars ystemso f higher vertebrates, and can be picturesquely interpreted as an interaction via idealized "subcortical nuclei" of portions of "cerebral cortex" with "neocerebellum". To the extent that this analogy is valid, various geometrical (anatomical) and dynamical (physiological) details of the networks can be interpreted as provisions by cerebrocerebellar systems for effective learning of spatiotemporal patterns. For example, we can interpret geometrical statements concerning excitatory cerebellar" network components to include: [...]
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other,
