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Exact neural mass model for synaptic-based working memory.

Halgurd Taher1, Alessandro Torcini2,3, Simona Olmi1,3

  • 1Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Sophia Antipolis, France.

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A new neural mass model explains working memory (WM) using synaptic plasticity, mimicking brain activity and offering insights into memory capacity and load measurement.

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Area of Science:

  • Computational neuroscience
  • Cognitive neuroscience
  • Neural modeling

Background:

  • Working memory (WM) theories often rely on persistent neural activity.
  • A synaptic theory offers an alternative, incorporating short-term synaptic plasticity.
  • Realistic cellular mechanisms are crucial for understanding network dynamics.

Purpose of the Study:

  • To develop a neural mass model for synaptic WM dynamics.
  • To link macroscopic network activity to Local Field Potential (LFP) and EEG signals.
  • To investigate memory capacity, load, and neural oscillations.

Main Methods:

  • Developed a neural mass model of spiking neural networks with synaptic plasticity.
  • Analyzed firing rate and mean membrane potential to model network dynamics.
  • Investigated effects of network architecture and item presentation rate on memory capacity.

Main Results:

  • Model reproduces WM operations via synaptic reactivation or persistent activity.
  • Simulated memory access/loading shows β-γ band oscillations, similar to experimental data.
  • Mean membrane potential serves as a proxy for memory load; γ power increases with item number.
  • Memory capacity is dependent on item presentation rate, with an optimal frequency range.

Conclusions:

  • Synaptic plasticity provides a viable mechanism for working memory.
  • The model offers insights into brain activity patterns (oscillations) during WM tasks.
  • Neural architecture and stimulus timing critically influence memory capacity.