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Canonical neural networks with delayed plasticity perform active inference and learning by minimizing future risks. This biologically inspired optimization achieves Bayes-optimal inference and control, offering insights into neural mechanisms for planning and behavior.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Bayesian inference

Background:

  • Canonical neural networks utilize rate coding models where neural activity and plasticity minimize a shared cost function.
  • Plasticity modulation occurs with a temporal delay in these networks.

Purpose of the Study:

  • To demonstrate that canonical neural networks implicitly perform active inference and learning.
  • To show these networks minimize risks associated with future outcomes.
  • To characterize neural networks in terms of Bayesian belief updating.

Main Methods:

  • Mathematical analysis to equate biological optimization with model evidence maximization (or variational free energy minimization).
  • Modeling neural networks as partially observed Markov decision processes.
  • Numerical simulations of maze tasks to validate the theory.

Main Results:

  • Delayed modulation of Hebbian plasticity and firing threshold adaptation are sufficient for Bayes-optimal inference and control.
  • Neural networks implicitly learn and adapt to minimize future outcome risks.
  • The proposed theory provides a universal characterization of canonical neural networks.

Conclusions:

  • Delayed plasticity and adaptation enable Bayes-optimal inference and control in neural networks.
  • This framework offers insights into the neuronal basis of planning and adaptive behavior.
  • Canonical neural networks can be understood as implementing Bayesian belief updating.