Related Experiment Video
Updated: Apr 7, 2026
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
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Synaptic unreliability facilitates information transmission in balanced cortical populations
Leon A Gatys1,2, Alexander S Ecker1,2,3,4, Tatjana Tchumatchenko5
1Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany.
Summary
Synaptic unreliability, a major source of brain noise, surprisingly aids information processing in balanced cortical circuits. This noise enhances the transmission of fast-varying signals, unlike typical stochastic resonance effects.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Biophysics
Background:
- Synaptic unreliability is a significant source of biophysical noise in neural systems.
- Understanding how neural systems process information despite this noise is a key challenge.
- Cortical circuits are characterized by a delicate balance between excitation and inhibition.
Purpose of the Study:
- To investigate the impact of synaptic noise on signal transmission in balanced cortical circuits.
- To determine whether synaptic variability impairs or facilitates neural information processing.
- To elucidate the underlying mechanisms of noise's effect on information transmission.
Main Methods:
- Computational modeling of neural circuits.
- Analysis of signal transmission under varying levels of synaptic noise.
- Information-theoretic measures to quantify signal transfer.
Main Results:
- Synaptic response variability enhances information transmission in balanced cortical circuits.
- Fast-varying signals benefit most, with noise instantaneously increasing shared information.
- The beneficial effect of noise operates via a general mechanism, not limited by an optimal noise level.
Conclusions:
- Synaptic noise, often considered detrimental, can be functionally beneficial in specific neural contexts.
- Balanced excitation-inhibition states may leverage noise to improve signal processing.
- This finding challenges conventional views on noise in neural computation and suggests novel mechanisms for information processing.
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