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Recurrence-mediated suprathreshold stochastic resonance.

Gregory Knoll1,2, Benjamin Lindner3,4

  • 1Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, Berlin, 10115, Germany. gregory.knoll@bccn-berlin.de.

Journal of Computational Neuroscience
|May 18, 2021
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Summary
This summary is machine-generated.

Suprathreshold stochastic resonance (SSR) in neural networks can be driven by network noise, not just intrinsic noise. Optimal signal transmission occurs when synaptic coupling strength is precisely controlled, demonstrating robust network performance.

Keywords:
RecurrenceSignal encodingSpiking networksSuprathreshold stochastic resonance

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Area of Science:

  • Computational neuroscience
  • Information theory

Background:

  • Feedforward networks (FFNs) exhibit suprathreshold stochastic resonance (SSR) due to intrinsic noise.
  • SSR is characterized by optimal signal transmission at a specific noise level.

Purpose of the Study:

  • To investigate if network noise, rather than intrinsic noise, can induce SSR in recurrent spiking networks.
  • To explore the role of synaptic coupling strength in controlling network noise and signal transmission.

Main Methods:

  • Simulated a recurrent spiking network.
  • Varied synaptic coupling strength to control network noise levels.
  • Performed control experiments with FFNs.

Main Results:

  • Demonstrated that SSR can be induced by network noise in recurrent networks.
  • Showed that coding fraction (information transmission) peaks at an optimal synaptic coupling strength.
  • Confirmed that optimized coding is due to noise level changes, not other coupling effects.

Conclusions:

  • Network noise can drive SSR, offering an alternative mechanism to intrinsic noise.
  • Synaptic coupling strength is a critical parameter for optimizing signal encoding in recurrent networks.
  • Temporally correlated network noise can enhance encoding performance beyond intrinsic white noise.