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Symmetry breaking in neural nets.

E Pessa1

  • 1Dipartimento di Matematica, Università degli Studi di Roma La Sapienza, Italy.

Biological Cybernetics
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Summary
This summary is machine-generated.

This study investigates Goldstone modes in neural nets after symmetry breaking. Approximate findings suggest these modes are crucial for storing complex inputs and generating ordered outputs when influenced by external stimuli.

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Area of Science:

  • Computational neuroscience
  • Theoretical physics

Background:

  • Neural networks exhibit symmetry-breaking transitions.
  • Goldstone modes are theoretical low-energy excitations in systems with spontaneous symmetry breaking.

Purpose of the Study:

  • To investigate the appearance and role of Goldstone modes in homogeneous neural network models after symmetry-breaking transitions.
  • To explore the potential significance of Goldstone modes in information processing within neural tissues.

Main Methods:

  • Analysis of two established homogeneous neural network models.
  • Study of symmetry-breaking transitions within these models.

Main Results:

  • Goldstone modes were observed in an approximate manner following symmetry breaking.
  • Indications suggest these modes become prominent under external input conditions.

Conclusions:

  • Goldstone modes may play a key role in enabling neural networks to store complex inputs.
  • The interaction with external inputs, mediated by Goldstone modes, is essential for generating ordered outputs.