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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Time-delayed feedback in neurosystems.

Eckehard Schöll¹, Gerald Hiller, Philipp Hövel

¹Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623 Berlin, Germany. schoell@physik.tu-berlin.de

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|February 17, 2009

Summary

This summary is machine-generated.

Time delays in coupled excitable neurons, modeled using the FitzHugh-Nagumo system, can control stochastic synchronization. Researchers found that adjusting delay times can enhance or suppress synchronization, leading to diverse oscillatory patterns.

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Area of Science:

Computational neuroscience
Nonlinear dynamics
Systems biology

Background:

Excitable systems, like neurons, exhibit complex dynamics.
Time delays are inherent in neural signaling and feedback loops.
Synchronization in neural networks is crucial for information processing.

Purpose of the Study:

To investigate the impact of time delays on the synchronization of coupled excitable neurons.
To explore how time delays influence oscillatory patterns and noise-induced synchronization.
To analyze the effects of local time-delayed feedback and self-feedback.

Main Methods:

Utilizing the FitzHugh-Nagumo model for simulating coupled excitable neurons.
Introducing time delays in both inter-neuronal coupling and self-feedback loops.
Applying white noise to study stochastic synchronization.
Systematically varying delay times and coupling strengths.

Main Results:

Time-delayed feedback can deliberately control stochastic synchronization, either enhancing or suppressing it.
Antiphase oscillations are achievable in delay-coupled neurons with sufficient delay and coupling.
Time-delayed self-feedback introduces complex behaviors like in-phase/antiphase oscillations, bursting, and amplitude death.

Conclusions:

Time delays are a critical parameter for controlling neural synchronization and dynamics.
The FitzHugh-Nagumo model with time delays effectively captures complex neural behaviors.
Understanding these dynamics is essential for deciphering neural communication and network function.