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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Jan 4, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing.

Guozhang Chen^1,2, Pulin Gong^3,4

¹School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia.

Nature Communications

|October 31, 2019

Summary

This summary is machine-generated.

Spontaneous brain activity is actively involved in cortical processing through a new mechanism called Computing by Modulating Spontaneous Activity (CMSA). This process speeds up neural responses and explains perceptual phenomena.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Cortical populations exhibit complex spontaneous spatiotemporal activity without sensory input.
The computational roles of this spontaneous activity are not fully understood.

Purpose of the Study:

To propose and investigate a novel neural computation mechanism: Computing by Modulating Spontaneous Activity (CMSA).
To elucidate how spontaneous activity is actively involved in cortical processing.

Main Methods:

Utilized biophysically plausible circuit models.
Simulated the modulation and redistribution of spontaneous activity patterns by external stimuli.

Main Results:

Demonstrated that CMSA generates neural responses by modulating spontaneous activity.
Identified significant computational advantages, including accelerated cortical processing.
Provided a unifying explanation for single-neuron and circuit-level experimental findings.
Linked CMSA to perceptual "bubbles" observed in psychophysical studies with natural stimuli like faces.

Conclusions:

CMSA is a fundamental mechanism for neural computation, actively utilizing spontaneous activity.
This mechanism offers computational benefits and explains diverse experimental observations.
CMSA provides a neurophysiological basis for perceptual phenomena like "bubbles".