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Behaviorally based modeling and computational approaches to neuroscience

G N Reeke1, O Sporns

  • 1Neurosciences Institute, New York, NY 10021.

Annual Review of Neuroscience
|January 1, 1993
PubMed
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Neural network and behavioral models offer insights into brain function and behavior control. Critical evaluation of these models, informed by a general theory of brain function, will refine our understanding of neuroscience.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Behavioral Science

Background:

  • Advances in computing power have spurred the development of numerous neural network and behavioral models.
  • The relevance of these models to experimental neuroscience varies, with some closely tied to biological data and others more abstract.

Purpose of the Study:

  • To survey and encourage critical comparison of diverse neural modeling approaches for understanding behavior.
  • To highlight the role of computational models in generating insights into neuronal control of behavior.

Main Methods:

  • Review of existing neural network and behavioral models.
  • Analysis of models based on neuroanatomy and neurophysiology.
  • Examination of models utilizing neurobiological principles in their architecture.

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Main Results:

  • Some models allow precise experimental predictions, while others offer broader explanatory insights into neuronal activity and behavior control.
  • Computational models provide a means to study behavior control mechanisms not easily accessible through traditional experimental methods.

Conclusions:

  • A critical evaluation phase for neural modeling has begun, essential for scientific rigor.
  • Integrating diverse models and evaluating them against comprehensive evidence will lead to a more robust set of neuroscientific principles.
  • The theory of neuronal group selection offers a promising framework for future modeling, aligning with evolutionary and physiological principles.