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Related Experiment Video

Updated: Jan 26, 2026

Stereotactically-guided Ablation of the Rat Auditory Cortex, and Localization of the Lesion in the Brain

Stereotactically-guided Ablation of the Rat Auditory Cortex, and Localization of the Lesion in the Brain

Published on: October 11, 2017

State-dependent geometry of population activity in rat auditory cortex.

Dmitry Kobak^1,2, Jose L Pardo-Vazquez^1,3, Mafalda Valente¹

¹Champalimaud Center for the Unknown, Lisbon, Portugal.

|April 11, 2019

Summary

Brain activity in the rat auditory cortex shows how neural population codes change with brain states. As the brain becomes more active, signal and noise become orthogonal, improving sound localization accuracy.

Keywords:

auditory cortex cortical state neuroscience population activity rat

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Last Updated: Jan 26, 2026

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Area of Science:

Neuroscience
Computational Neuroscience
Auditory Neuroscience

Background:

Neural population codes are crucial for accurate sensory processing.
Understanding the high-dimensional geometry of signal and noise subspaces is limited.
Brain states significantly influence neural coding strategies.

Purpose of the Study:

To empirically characterize the geometry of population codes in the rat auditory cortex.
To investigate how brain states affect signal and noise subspace organization.
To determine the impact of these changes on sound lateralization decoding.

Main Methods:

Studied population codes in rat auditory cortex across activation-inactivation brain states.
Utilized sounds varying in interaural level differences and mean level.
Analyzed the geometry of signal and noise subspaces in neural activity.

Main Results:

Cortical activation led to a shift from contralateral sound preference to symmetric preference.
Gain modulation decreased, and signal/noise subspaces became orthogonal to global activity.
Level-invariant decoding of sound lateralization emerged in the active state.

Conclusions:

Cortical population codes exhibit state-dependent geometric properties.
The orthogonality of signal and noise subspaces supports robust sensory processing.
These findings provide an empirical basis for understanding cortical population coding.