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Jakob Jordan1,2, Mihai A Petrovici3,4, Oliver Breitwieser4

  • 1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain-Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany. jordan@pyl.unibe.ch.

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Shared noise in neuronal networks impairs performance. A deterministic recurrent network can generate noise that overcomes this, improving probabilistic computations in large neural ensembles.

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Area of Science:

  • Computational neuroscience
  • Neural network modeling

Background:

  • Neuronal network models for high-level brain functions often incorporate noise.
  • Uncorrelated external noise is a common assumption, but biological networks face challenges with finite noise sources.

Purpose of the Study:

  • To investigate the impact of shared noise correlations in neuronal network models.
  • To propose a novel method for generating effective noise in large-scale neural simulations.

Main Methods:

  • Analyzing the performance of stochastic network models with finite independent noise sources.
  • Implementing a deterministic recurrent neuronal network as a noise generator.
  • Investigating the role of inhibitory feedback in shaping noise characteristics.

Main Results:

  • Shared noise correlations from finite sources significantly degrade model performance.
  • A deterministic recurrent network effectively suppresses detrimental shared input effects.
  • This recurrent network can serve as a natural noise source for large functional networks.

Conclusions:

  • Finite noise sources introduce performance-limiting correlations in neural models.
  • Deterministic recurrent networks offer a solution by generating spatially correlated noise.
  • This approach enables robust probabilistic computations in large-scale neural ensembles.