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Aligned and oblique dynamics in recurrent neural networks.

Friedrich Schuessler1,2, Francesca Mastrogiuseppe3, Srdjan Ostojic4

  • 1Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany.

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|November 27, 2024
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Summary

Researchers used recurrent neural networks (RNNs) to study neural representations. They discovered two distinct dynamical regimes, aligned and oblique, offering new ways to interpret neural activity and its relation to behavior.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • The relationship between neural activity and behavior is fundamental to neuroscience.
  • Partial dissociations between neural activity and external variables suggest complex internal dynamics.
  • A theoretical framework explaining these relationships is currently lacking.

Purpose of the Study:

  • To explore the geometrical relationship between neural dynamics and network output using recurrent neural networks (RNNs).
  • To understand the conditions and mechanisms governing neural representations and their potential dissociations.

Main Methods:

  • Utilized recurrent neural networks (RNNs) to model neural dynamics.
  • Analyzed network outputs from geometrical and dynamical stability perspectives.
  • Investigated the role of readout weight magnitude as a control parameter.
  • Examined neural recordings for evidence of distinct dynamical regimes.

Main Results:

  • Identified two dynamical regimes in RNNs: aligned and oblique.
  • Demonstrated that readout weight magnitude controls the transition between regimes.
  • Showed that oblique networks exhibit greater heterogeneity, noise suppression, and robustness.
  • Found the oblique regime to be specific to recurrent networks due to dynamical stability.
  • Observed dissociations in neural recordings corresponding to these regimes.

Conclusions:

  • The study provides a new geometrical framework for understanding neural representations.
  • Findings suggest that the oblique regime in RNNs offers advantages for information processing and robustness.
  • The results offer a novel perspective for interpreting neural activity in relation to network dynamics and behavior.