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Related Experiment Video

Updated: Mar 29, 2026

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A stochastic model of input effectiveness during irregular gamma rhythms.

Grégory Dumont1,2,3, Georg Northoff4,5, André Longtin6,7

  • 1Physics Department, 150 Louis Pasteur Ottawa, Ottawa, Ontario, K1N 6N5, Canada. gdumont@uottawa.ca.

Journal of Computational Neuroscience
|November 28, 2015
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This study explores how neural network rhythms, specifically gamma-band synchronization, are affected by noisy inputs. It reveals that "communication through coherence" is sensitive to variability, impacting brain region communication.

Keywords:
Communication through coherenceGamma oscillationsStimulus selection

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Area of Science:

  • Computational neuroscience
  • Neural dynamics
  • Systems neuroscience

Background:

  • Gamma-band synchronization is crucial for attention and inter-regional brain communication.
  • The precise dynamical mechanisms governing gamma rhythms and their role in information processing remain unclear.
  • Understanding how neural networks process information via rhythmic activity is essential.

Purpose of the Study:

  • To investigate the dynamical mechanisms of gamma-band synchronization in neural networks.
  • To model how input timing and amplitude affect gamma rhythm generation and network activity.
  • To explore the resilience of "communication through coherence" (CTC) in the presence of neural and input variability.

Main Methods:

  • Developed a computationally efficient stochastic modeling approach for excitatory-inhibitory (E-I) neural networks.
  • Utilized stochastic two-state neurons exhibiting finite-size fluctuations.
  • Employed the Hilbert transform and Kuramoto index to analyze rhythm entrainment and phase.
  • Calculated the effectiveness of external inputs onto pyramidal (E) cells under rhythmic inhibition.

Main Results:

  • The model demonstrates a fast computation of input effectiveness in gamma rhythms.
  • Communication through coherence (CTC) is sensitive to spike arrival time jitter and interneuron (I) cell entrainment.
  • CTC can persist even in systems without a deterministic oscillation, relying on noise-induced quasi-cycles.
  • A trade-off exists between phase selectivity and rate modulation depth for transmitting input variations.
  • Variability in rhythm and input significantly decreases phase preference, impacting CTC robustness.

Conclusions:

  • Stochastic modeling provides a rapid tool for investigating CTC in noisy neural systems.
  • CTC is vulnerable to variability, highlighting the importance of precise timing and network stability.
  • Interneuron pacing plays a critical role in gamma rhythm generation.
  • The findings offer insights into how brain regions communicate effectively despite inherent neural noise.