Variability and Chaos: Neurointegrative Principles in Self-Organization of Motor Patterns

Abstract

We discuss the possibility that variability may be a central feature of self-organizing processes. We suggest that variability may be inherently part of the mechanisms by which adaptive neurocircuits emerge, and contrast such functional neurocircuits against definitions involving anatomical or dynamical structures which the self-organizational definition both contains and supercedes. The experimental work focuses on an invertebrate animal, the sea slug, Pleurobranchaea californica, which has a rich behavioral repertoire of buccal/oral behaviors, and a relatively simple nervous system containing identifiable neurons. We present evidence from work on a set of 20 neurons, which we refer to as BCNs (buccal-cerebral neurons), that communicate between the buccal ganglion and cerebral ganglion. These neurons are crucial for generating all buccal/oral behaviors, and provide an advantageous source of experimental material for inquiring into the self-organization of group activity. Variability in the activity of the BCNs, and in the motoneurons that they drive, is attributable to low-dimensional chaos. These findings indicate that some variability may arise from the same mechanisms that generate the patterned activity: i.e., that the observed variations are not noise that is superimposed on the code underlying a behavior. The role of sensory feedback in the production of adaptive behavior of animals as they interact with complex and often unpredictable environments is discussed and we suggest that chaotic neural activity provides a means for the nervous system to create new informational space rendering animals more stably adaptable in such changing environments.

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Document Details

Document Type
Technical Report
Publication Date
Mar 15, 1989
Accession Number
ADA208864

Entities

People

  • C. S. Cohan
  • G. J. Mpitsos
  • H. C. Creech
  • M. Mendelson

Organizations

  • Oregon State University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Animals
  • Brain
  • Cells
  • Central Nervous System
  • Computational Science
  • Computer Programs
  • Computers
  • Equations
  • Human Behavior
  • Mathematical Models
  • Models
  • Nervous System
  • Parallel Computing
  • Parallel Processing
  • Phase Transformations
  • Production

Fields of Study

  • Psychology

Readers

  • Neuroscience
  • Systems Analysis and Design

Technology Areas

  • Space