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Related Experiment Video

Updated: Mar 13, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex.

Gergő Orbán¹, Pietro Berkes², József Fiser³

¹Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; MTA Wigner Research Center for Physics, Budapest 1121, Hungary.

|October 21, 2016

Summary

This summary is machine-generated.

Neural variability in the visual cortex encodes perceptual uncertainty. A new theory shows that population activity patterns, representing statistical samples, explain neural noise and correlations, suggesting a novel computational role.

Keywords:

Bayesian computations V1 natural images noise correlations normative model spontaneous activity stochastic sampling theory variability vision

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Last Updated: Mar 13, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

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Published on: May 12, 2019

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Topographical Estimation of Visual Population Receptive Fields by fMRI

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Published on: February 3, 2015

Area of Science:

Neuroscience
Computational Neuroscience
Visual Cortex Research

Background:

Neural responses in the visual cortex exhibit significant variability.
Existing theories of cortical computation inadequately explain this stimulus-dependent variability.
Current models often ignore variability or only address its basic aspects.

Purpose of the Study:

To develop a novel theory of cortical computation that incorporates neural variability.
To propose that perceptual uncertainty is encoded by response variability, not average response magnitude.
To test a sampling-based probabilistic representation against empirical data from the visual cortex.

Main Methods:

Developing a probabilistic inference theory for cortical computation.
Modeling population activity as statistical samples from an inferred distribution.
Analyzing existing and original data from the primary visual cortex.

Main Results:

A sampling-based probabilistic representation effectively explains neural variability.
This model accounts for the structure of noise, signal, and spontaneous response variability.
It also explains response correlations within the primary visual cortex.

Conclusions:

Neural variability plays a crucial role in cortical dynamics and computation.
Perceptual uncertainty is directly encoded by the variability of neural responses.
The findings support a probabilistic, sampling-based framework for understanding visual cortex function.