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Synaptic pruning facilitates online Bayesian model selection.

Ukyo T Tazawa1, Takuya Isomura2

  • 1Brain Intelligence Theory Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351- 0198, Japan; Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto-shi, Kyoto, 606-8501, Japan; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Synaptic pruning, a process where connections are removed, helps brains and AI efficiently learn environmental structures. This Bayesian synaptic model pruning (BSyMP) method optimizes models by removing uninformative connections.

Keywords:
Bayesian model reductionBayesian model selectionFree-energy principleStructure learningSynaptic pruning

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Artificial intelligence

Background:

  • Identifying optimal structures for generative or world models is crucial for biological and artificial systems.
  • Statistical structure learning is fundamental for efficient information processing.

Purpose of the Study:

  • To introduce a novel synaptic pruning scheme, Bayesian synaptic model pruning (BSyMP), for efficient statistical structure learning.
  • To demonstrate BSyMP's effectiveness in identifying plausible environmental model structures, especially with sparse data.

Main Methods:

  • Extended canonical neural networks to develop a synaptic pruning scheme.
  • Formally equated the scheme to online Bayesian model selection.
  • Utilized connectivity parameters to enable/disable synaptic connections (ON/OFF states).

Main Results:

  • BSyMP parameters converge to zero for uninformative connections, enabling efficient model reduction.
  • BSyMP identified plausible environmental model structures in environments with sparse likelihood and transition matrices.
  • BSyMP demonstrated more efficient model reduction compared to conventional Bayesian model reduction schemes in simulations.

Conclusions:

  • Synaptic pruning is a viable neuronal substrate for structure learning and generalizability in the brain.
  • BSyMP offers a reliable and efficient method for model reduction and structure identification in artificial systems.
  • The findings bridge understanding between biological learning mechanisms and artificial model optimization.