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Related Experiment Video

Updated: Jun 6, 2026

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

Published on: March 31, 2016

Irregular dynamics in up and down cortical states.

Jorge F Mejias¹, Hilbert J Kappen, Joaquin J Torres

¹Centre for Neural Dynamics, University of Ottawa, Ottawa, Ontario, Canada. jmejias@uottawa.ca

|November 17, 2010

Summary

This summary is machine-generated.

Neural systems exhibit complex dynamics, like voltage transitions between up and down states. Our model shows synaptic noise and recovery times drive these transitions, explaining power-law distributed up-state durations.

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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

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Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

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Published on: August 1, 2011

Area of Science:

Computational neuroscience
Neural dynamics modeling

Background:

Neural systems display complex coherent dynamics, exemplified by voltage transitions between up and down states in cortical areas.
Understanding these state transitions is crucial for comprehending brain function.

Purpose of the Study:

To investigate the mechanisms underlying up and down state transitions in neural systems.
To develop and analyze a biologically motivated stochastic model of these transitions.

Main Methods:

Constructed a stochastic model featuring bistable rate dynamics and short-term synaptic processes.
Employed mean-field approaches and numerical simulations for comprehensive analysis.
Investigated the role of synaptic noise and temporal correlations in driving state transitions.

Main Results:

The model successfully reproduces complex transitions between high (up) and low (down) neural activity states.
Up-state permanence times were found to follow a power-law distribution.
Demonstrated that synaptic noise and substantial recovery times are essential for power-law distributions.

Conclusions:

Biologically plausible synaptic noise and temporal correlations are key drivers of complex neural dynamics.
The model explains experimentally observed fluctuations in up and down state durations.
Static synapses or noise-free dynamical synapses cannot replicate these complex dynamic behaviors.