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Observer-participant models of neural processing.

R L Fry1

  • 1Appl. Phys. Lab., Johns Hopkins Univ., Baltimore, MD.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
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This study models neurons as information channels, where learning defines the neuron's question. Applying information theory leads to the Hopfield neuron model with conditionalized Hebbian learning rules.

Area of Science:

  • Computational neuroscience
  • Information theory
  • Machine learning

Background:

  • Neurons process information, but their function as information channels with inherent distortion is complex.
  • Understanding neural decision-making requires models that account for input-output mappings and learning.

Purpose of the Study:

  • To propose a model where neurons function as information channels.
  • To investigate how learning shapes neural decision-making processes.
  • To apply information-theoretic measures to derive a specific neural network model.

Main Methods:

  • Modeling the neuron as a many-to-one information channel with two output states.
  • Defining learning as the process of determining an appropriate question based on input ensembles.

Related Experiment Videos

  • Applying Shannon information measures (entropy, mutual information) to the model.

    • Main Results:

    • The proposed model leads to the Hopfield neuron model with conditionalized Hebbian learning.
    • Neural decisions are characterized by a sigmoidal transfer function.
    • In the limit of zero computational temperature, decisions follow a maximum likelihood rule.

    Conclusions:

    • The neuron's decision-making can be understood through an information-theoretic lens.
    • Learning is crucial for defining the 'question' a neuron answers.
    • The model provides a novel perspective contrasting with Linsker's information-theoretic approach.