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Updated: Jun 28, 2026
08:02
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
Published on: August 14, 2015
Noise-memory induced excitability and pattern formation in oscillatory neural models
Erik Glatt1, Hauke Busch, Friedemann Kaiser
1Institute of Applied Physics, Darmstadt University of Technology, 64289 Darmstadt, Germany.
Summary
Noise-memory induced phase transitions suppress synchronous oscillations in neural systems, restoring excitable dynamics. This finding highlights how temporally correlated noise can stabilize unstable systems and enable information transmission.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Complex Systems
Background:
- Oscillatory neural systems often exhibit synchronous behavior.
- Understanding the impact of noise on system dynamics is crucial.
- Deterministic instability can limit functional capabilities.
Purpose of the Study:
- Investigate noise-memory induced phase transitions in oscillatory neural systems.
- Analyze the suppression of synchronous oscillations and restoration of excitable dynamics.
- Explore the role of temporally correlated noise in stabilizing unstable fixed points.
Main Methods:
- Simulations of globally coupled oscillatory neural arrays.
- Analysis of temporally correlated parametric noise effects.
- Development of an analytic approach for exponentially correlated noise.
Main Results:
- Noise-memory induced phase transition observed, suppressing synchronous oscillations.
- Restoration of excitable dynamics and information transmission capabilities.
- Reentrant phase transition to noise-induced excitability by altering noise correlation time.
Conclusions:
- Temporally correlated noise can induce phase transitions and stabilize neural systems.
- Noise-induced excitability restores information processing in neural arrays.
- Analytic predictions align with simulation results for noise effects.
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