Investigation of Dynamic Algorithms for Pattern Recognition Identified in Cerebral Cortex.

摘要

Patterns of 40 to 80 Hz oscillation have been observed by researchers of this laboratory in the large scale activity not only of olfactory cortex, but also visual neocortex, and shown to predict the olfactory and visual pattern recognition responses of a trained animal. Similar observations of 40 Hz oscillation in auditory and motor cortex, and in the retina and EMG have been reported. It thus appears that cortical computation in general may occur by dynamical interaction of resonant modes; as we have long thought to be the case in the olfactory system. The oscillation can serve a macroscopic clocking function and entrain or bind the relevant microscopic activity of disparate cortical regions into a well defined phase coherent collective state of gestalt . This can overide irrelevant microscopic activity and produce coordinated motor output. We have further evidence that the oscillatory activity is roughly periodic, but actually appears to be chaotic (nonperiodic) when examined in detail. If this view is correct, then networks with oscillatory and possibly chaotic activity form the actual cortical substrate of the diverse sensory, motor, and cognitive operations now studied in static networks. It must then be shown how those functions can be accomplished with oscillatory and chaotic dynamics. It is our expectation that nature makes good use of this dynamical complexity, and our intent has been to search here for novel design principles that may underly the superior performance of biological systems in pattern recognition. These may then be applied in artificial systems to engineering problems.