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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Localist neural plasticity identified by mutual information.

Gabriele Scheler1, Martin L Schumann2, Johann Schumann3

  • 1Carl Correns Foundation for Mathematical Biology, 1030 Judson Dr, Mountain View, CA, 94040, USA. gscheler@gmail.com.

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|March 22, 2025
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This study introduces a biologically realistic neural network model for pattern memory and retrieval. By stimulating high mutual-information (MI) neurons, the model achieves efficient and reliable recall of stored patterns.

Keywords:
Cortical modelInformation theoryPattern memorySymbolic abstraction

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Understanding biological memory mechanisms is crucial for developing advanced AI.
  • Cortical networks exhibit complex dynamics for information processing and storage.
  • Existing models often lack biological realism or efficient learning mechanisms.

Purpose of the Study:

  • To develop a biologically realistic neural network model for pattern memory and retrieval.
  • To identify and utilize high mutual-information (MI) neurons for efficient pattern storage and recall.
  • To investigate the plasticity and information dynamics within the network during learning.

Main Methods:

  • Utilized a cortex-like balanced inhibitory-excitatory network with heterogeneous neurons.
  • Implemented a one-shot adaptive learning process focused on high MI neurons and inhibition ('localist plasticity').
  • Assessed pattern representation quality before learning, after learning, and after recall via stimulation.

Main Results:

  • Identified high MI neurons as key information carriers for pattern representation.
  • Demonstrated efficient pattern storage (k=10 patterns, s=400) in a 1000/1200 neuron network.
  • Achieved reliable pattern recall by stimulating only high MI neurons, with recalled patterns showing high similarity to original inputs.
  • Observed a shift in neuron property distribution from Gaussian to lognormal during adaptation.

Conclusions:

  • The model successfully demonstrates biologically realistic pattern memory and retrieval.
  • Stimulating high MI neurons is a viable strategy for efficient pattern recall.
  • The 'localist plasticity' approach offers high learning efficiency.
  • The model has potential applications in artificial intelligence and understanding brain function.