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Embedding responses in spontaneous neural activity shaped through sequential learning.

Tomoki Kurikawa1, Kunihiko Kaneko

  • 1Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, Japan. kurikawa@complex.c.u-tokyo.ac.jp

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This study proposes a new "memories-as-bifurcations" model for neural networks, suggesting spontaneous neural activity aids memory recall. This approach enhances learning capacity by shaping neural dynamics for targeted outputs.

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Area of Science:

  • Computational Neuroscience
  • Dynamical Systems Theory
  • Machine Learning

Background:

  • Spontaneous neural activity exhibits complex spatiotemporal structure and influences responses to external stimuli.
  • Traditional models view memories as attractors, but this work explores an alternative perspective.

Purpose of the Study:

  • To propose and investigate the
  • memories-as-bifurcations
  • viewpoint for understanding memory recall and learning in neural systems.
  • To introduce a novel associative memory model based on this viewpoint.

Main Methods:

  • Developed a computational model incorporating a sequential learning process using Hebbian-type learning.
  • Analyzed the neural dynamics, focusing on bifurcations and chaotic behavior in spontaneous activity.
  • Simulated input/output mappings to evaluate memory storage and recall capabilities.

Main Results:

  • The model successfully memorizes numerous input/output mappings.
  • Learned neural dynamics exhibit bifurcations, stabilizing target patterns upon input.
  • Spontaneous neural activity displays chaotic dynamics with occasional proximity to memorized patterns, facilitating recall.

Conclusions:

  • The
  • memories-as-bifurcations
  • framework provides a new understanding of neural memory and learning.
  • The proposed model demonstrates enhanced learning capacity through shaped neural dynamics.
  • Findings align with experimental observations of spontaneous neural activity patterns.