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Related Experiment Video

Updated: Mar 16, 2026

Using Looming Visual Stimuli to Evaluate Mouse Vision

Using Looming Visual Stimuli to Evaluate Mouse Vision

Published on: June 13, 2019

Nonuniform spike count shared variability in the mouse early visual system.

Jordan L Hickman¹, Juan Santiago Moreno¹, Grant Hughes¹

¹Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States.

Journal of Neurophysiology

|March 14, 2026

Summary

This summary is machine-generated.

Shared neural variability in the visual cortex (V1) and lateral geniculate nucleus (dLGN) can influence sensory information coding. This study reveals how shared variability impacts visual information processing across different timescales.

Keywords:

neural coding variability visual cortex visual information

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Sensory stimuli are encoded by the collective activity of neuronal populations in the mammalian cortex.
Trial-to-trial variability in neural responses, particularly non-independent variability between neurons, can modulate the amount of sensory information conveyed by population activity.
The structure, sources, and impact of shared variability at various timescales on visual information processing remain incompletely understood.

Purpose of the Study:

To investigate the structure and functional implications of shared neural variability in the primary visual cortex (V1) and lateral geniculate nucleus (dLGN) of awake mice.
To determine how shared variability at different timescales affects the coding of visual information.
To compare shared variability under natural visual drive versus artificial electrical stimulation.

Main Methods:

Simultaneous recording of tens to hundreds of single neurons in V1 and dLGN using Neuropixels probes in awake mice.
Measurement of global and layer-specific shared spike count variability across timescales (millisecond to second).
Comparison of shared variability during visually-evoked responses versus electrically-evoked cortical responses.

Main Results:

Shared variability in neural populations was found to be low-dimensional.
Depending on the timescale, shared variability could facilitate synergistic or redundant coding.
Computational decoders could leverage shared variability to improve information transmission at fine timescales (< 10 msec).
Shared variability had a negligible impact on information at longer timescales (100 msec) in V1.

Conclusions:

Information regarding visual inputs is present within shared neural variability, but it is not uniformly distributed.
Shared variability's impact on visual information coding is timescale-dependent and varies across neural ensembles within V1.