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Precise spatial memory in local random networks.

Joseph L Natale1, H George E Hentschel1, Ilya Nemenman2

  • 1Department of Physics, Emory University, Atlanta, Georgia 30322, USA.

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Summary
This summary is machine-generated.

This study demonstrates a novel neural network model that uses random connectivity to achieve spatial working memory. This approach bypasses the need for highly structured synapses, offering a more biologically plausible mechanism for persistent neural activity.

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Area of Science:

  • Computational neuroscience
  • Cognitive neuroscience

Background:

  • Persistent neuronal activity is crucial for working memory and spatial representations.
  • Continuous-attractor neural networks (CANNs) are a common model for persistent activity but often require structured synaptic architectures.

Purpose of the Study:

  • To investigate a novel neural network model for spatial working memory using random connectivity.
  • To demonstrate that global regulation of mean firing rate can produce discrete attractors for spatial encoding.
  • To analyze the storage capacity and retrievability of spatial information in this model.

Main Methods:

  • Numerical simulations of a geometrically embedded neural network model.
  • Implementation of a local, random connectivity profile.
  • Application of global regulation of the mean firing rate.
  • Analysis of attractor dynamics and network storage capacity.

Main Results:

  • Localized, finely spaced discrete attractors were generated, spanning a two-dimensional manifold.
  • The network reliably encoded spatial input locations via attractor dynamics without synaptic fine-tuning.
  • Numerical measurements showed storage capacity equivalent to a full tiling of the plane, similar to models with translationally invariant synapses.

Conclusions:

  • A computationally efficient model for spatial working memory using random connectivity and global rate regulation is presented.
  • This model offers a biologically plausible alternative to highly structured CANNs for representing spatial information.
  • The findings have implications for understanding visuospatial working memory and developing new neural network architectures.