A biological model for construction of meaning to serve as an interface between an intelligent system and its environments
- Univ of California, Berkeley, CA (United States)
There are two main levels of neural function to be modeled with appropriate state variables and operations. Microscopic activity is seen in the fraction of the variance of single neuron pulse trains (>99.9%) that is largely random and uncorrelated with pulse trains of other neurons in the neuropil. Macroscopic activity is revealed in the >0.1% of the total variance of each neuron that is covariant with all other neurons in neuropil comprising a population. It is observed in dendritic potentials recorded as surface EEGs. The {open_quotes}spontaneous{close_quotes} background activity of neuropil at both levels arises from mutual excitation within a population of excitatory neurons. Its governing point attractor is set by the macroscopic state, which acts as an order parameter to regulate the contributing neurons. The point attractor manifests a homogeneous field of white noise, which can be modeled by a continuous time state variable for pulse density. Neuropil comprises both excitatory and inhibitory neurons Their interactions at the macroscopic level give oscillations, manifesting a limit cycle attractor. Multiple areas of neuropil comprising a sensory system interact. Due to their incommensurate characteristic frequencies and the long axonal delays between them, the system maintains a global chaotic attractor having multiple wings, one for each discriminable class of stimuli. Access to each wing is by stimulus- induced state transitions, causing construction of macroscopic chaotic patterns, that are carried to targets of cortical transmission by axon tracts. AM patterns of the carrier are extracted by the targets by spatiotemporal integration, thereby retrieving the covariance comprising the chaotic signal. In digital models, noise serves to stabilize the chaotic attractors. An example will be given of the model operating as an interface between the environment and a pattern classifier, which learns to form its own feature detectors.
- OSTI ID:
- 466433
- Report Number(s):
- CONF-9610138-; TRN: 97:001309-0012
- Resource Relation:
- Conference: International multi-disciplinary conference on intelligent systems: a semiotic perspective, Gaithersburg, MD (United States), 21-23 Oct 1996; Other Information: PBD: 1996; Related Information: Is Part Of Intelligent systems: A semiotic perspective. Volume I: Theoretical semiotics; Albus, J.; Meystel, A.; Quintero, R.; PB: 303 p.
- Country of Publication:
- United States
- Language:
- English
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