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Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.

Yaroslav Felipe Kalle Kossio1, Sven Goedeke1, Christian Klos1

  • 1Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, 53115 Bonn, Germany.

Proceedings of the National Academy of Sciences of the United States of America
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

Memories persist despite constant neural changes. A new model shows how neuronal assemblies dynamically remodel over time, maintaining stable memories through continuous neuron exchange and plasticity.

Keywords:
associative memorycell assembliesneural representationsrepresentational driftsynaptic remodeling

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Neuroscience

Background:

  • Living organisms exhibit constant change, including in neural connections (connectome) and representations.
  • Persistent behaviors and memories contrast with this dynamic neural landscape.
  • Standard models assume static neuronal assemblies for memory storage.

Purpose of the Study:

  • To propose a novel memory model that accounts for temporal remodeling of neuronal assemblies.
  • To explain how persistent memories can be maintained despite constant neural changes.
  • To reconcile experimental observations of evolving neural representations with stable memory recall.

Main Methods:

  • Developing a computational model of associative memory with complete temporal remodeling.
  • Simulating the effects of autonomous network activity and synaptic turnover on neuronal assemblies.
  • Incorporating activity-dependent and homeostatic plasticity mechanisms.
  • Analyzing the stability of representational structure under dynamic conditions.

Main Results:

  • Neuronal assemblies undergo gradual neuron exchange due to network noise and synaptic turnover.
  • This dynamic remodeling conserves the representational structure of memories.
  • The model successfully explains the temporal evolution of fear memory representations.
  • Persistent memory is achieved through continuous adaptation of neuronal assemblies.

Conclusions:

  • Memory systems are dynamic and require understanding their complete, evolving nature.
  • Complete temporal remodeling of neuronal assemblies is a viable mechanism for persistent memory.
  • Constant change in individual neural components does not preclude long-term memory stability.
  • The proposed model offers a new perspective on neural plasticity and memory consolidation.