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Auditory cortical neurons convey maximal stimulus-specific information at their best frequency.

ABSTRACT: Sensory neurons are often thought to encode information about their preferred stimuli. It has also been proposed that neurons convey the most information about stimuli in the flanks of their tuning curves, where firing rate changes most steeply. Here we demonstrate that the responses of rat auditory cortical neurons convey maximal stimulus-specific information about sound frequency at their best frequency, rather than in the flanks of their tuning curves. Theoretical work has shown that stimulus-specific information shifts from tuning curve slope to peak as neuronal variability increases. These results therefore suggest that with respect to the most informative regions of the tuning curve, auditory cortical neurons operate in a regime of high variability.

SUBMITTER: Montgomery N

PROVIDER: S-EPMC3838392 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Auditory cortical neurons convey maximal stimulus-specific information at their best frequency.

Montgomery Nathan N Wehr Michael M

The Journal of neuroscience : the official journal of the Society for Neuroscience 20101001 40

Sensory neurons are often thought to encode information about their preferred stimuli. It has also been proposed that neurons convey the most information about stimuli in the flanks of their tuning curves, where firing rate changes most steeply. Here we demonstrate that the responses of rat auditory cortical neurons convey maximal stimulus-specific information about sound frequency at their best frequency, rather than in the flanks of their tuning curves. Theoretical work has shown that stimulus ...[more]

PMID: 20926662

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Project description:Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Dataset Information

Auditory cortical neurons convey maximal stimulus-specific information at their best frequency.

Publications

Auditory cortical neurons convey maximal stimulus-specific information at their best frequency.

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