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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: Jun 5, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Decoding the activity of neuronal populations in macaque primary visual cortex.

Arnulf B A Graf¹, Adam Kohn, Mehrdad Jazayeri

¹Center for Neural Science, New York University, New York, New York, USA. graf@vis.caltech.edu

Nature Neuroscience

|January 11, 2011

Summary

Combining signals from multiple visual cortex neurons improves accuracy. An empirical decoder using correlated variability outperformed a simple decoder, showing how neuronal response structure aids sensory decoding for better perception.

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Area of Science:

Neuroscience
Computational Neuroscience
Vision Science

Background:

Visual function relies on accurate neuronal signals from the visual cortex.
Combining information from multiple neurons enhances accuracy due to individual neuronal response variability.

Purpose of the Study:

To investigate the reliability of information extracted from simultaneous recordings of neuronal populations in the macaque primary visual cortex.
To evaluate a decoding framework for inferring visual stimuli from population activity patterns.

Main Methods:

Developed and tested a decoding framework using linear combination of neuronal responses.
Compared a simple parametric decoder (assuming neuronal independence) with a sophisticated empirical decoder that accounts for correlated variability.
Assessed performance for orientation estimation and discrimination tasks.

Main Results:

The empirical decoder, incorporating correlated neuronal variability, significantly outperformed the parametric decoder.
The structure of neuronal response distributions contains crucial information for effective sensory decoding.
Population activity patterns provide a reliable basis for inferring visual stimuli.

Conclusions:

Neuronal response structure, including correlated variability, is vital for accurate sensory decoding.
Sophisticated decoding models that leverage this structure can improve the inference of visual stimuli from population activity.
Understanding population coding enhances our knowledge of how perceptual decisions are informed by neural signals.