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Related Experiment Videos

Identification models of the nervous system.

D Zipser1

  • 1University of California, San Diego, Department of Cognitive Science, La Jolla 92093-0515.

Neuroscience
|January 1, 1992
PubMed
Summary

Supervised learning models of the brain closely mimic real neurons. This study frames these artificial neural networks as a system identification problem, enabling realistic neural modeling.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Neuroscience

Background:

  • Artificial neural networks (ANNs) trained via supervised learning often exhibit neuron behavior similar to biological neurons.
  • The reasons for this resemblance and the utility of such models are not fully understood.

Purpose of the Study:

  • To review and analyze recent developments in supervised learning models of the brain.
  • To clarify the relationship between ANNs and neural computation.
  • To establish a framework for generating realistic neural models.

Main Methods:

  • Treating supervised learning models as a specific instance of system identification.
  • Utilizing a general and well-established modeling paradigm.
  • Incorporating high-level computational descriptions and neurobiological constraints.

Main Results:

  • System identification provides a systematic approach to creating realistic neural models.
  • This paradigm allows for the integration of detailed architectural and physiological constraints.
  • It offers a structured method for model generation, avoiding ad hoc algorithm discovery.

Conclusions:

  • Supervised learning models can be effectively understood through the lens of system identification.
  • This approach facilitates the creation of neurobiologically plausible and data-fitting neural models.
  • It offers a powerful tool for neuroscientists to build and validate computational models of the brain.

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