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Learning neural dynamics through instructive signals.

Rich Pang1,2, Juncal Arbelaiz1,3, Jonathan W Pillow1

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

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|September 15, 2025
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Summary
This summary is machine-generated.

A new PRISM plasticity rule enables rapid learning of complex brain dynamics. This mechanism, guided by instructive signals, advances understanding of neural computation and machine learning.

Keywords:
cerebellumhippocampusinstructive signalsmushroom bodyneural dynamicsplasticityrapid learningrecurrent neural network

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Rapid learning is crucial for flexible behavior, but its neural basis is not fully understood.
  • Existing synaptic plasticity rules have limitations in explaining fast, adaptive learning.
  • The hippocampus, cerebellum, and mushroom body utilize distinct plasticity mechanisms.

Purpose of the Study:

  • To introduce a unifying mechanistic model, the PRISM plasticity rule, for fast-acting synaptic plasticity.
  • To investigate how PRISM plasticity, guided by instructive signals, facilitates learning complex neural dynamics.
  • To explore the application of PRISM plasticity in artificial learning algorithms for temporal credit assignment.

Main Methods:

  • Developed a multi-region network model incorporating the PRISM plasticity rule.
  • Utilized comprehensive simulations and exact mathematical theory to validate the model.
  • Analyzed the rule's performance in learning nonlinear dynamics and emulating external system dynamics.

Main Results:

  • PRISM plasticity, driven by pre-synaptic activity and instructive signals, enables rapid learning of flexible nonlinear dynamics.
  • The model successfully emulates unknown external system dynamics using real-time error signals.
  • PRISM plasticity demonstrates superior performance in learning general-purpose neural computations compared to Hebbian rules.

Conclusions:

  • PRISM plasticity provides a unified mechanistic explanation for fast synaptic plasticity across different brain regions.
  • Instructive signals are key to enabling rapid and flexible learning of complex neural computations.
  • Incorporating PRISM plasticity into AI can address long-standing challenges like temporal credit assignment in machine learning.