Cortical Inhibition Reduces Information Redundancy at Presentation of Communication Sounds in the Primary Auditory Cortex

Abstract

In all sensory modalities, intracortical inhibition shapes the functional properties of cortical neurons but also influences the responses to natural stimuli. Studies performed in various species have revealed that auditory cortex neurons respond to conspecific vocalizations by temporal spike patterns displaying a high trial-to-trial reliability, which might result from precise timing between excitation and inhibition. Studying the guinea pig auditory cortex, we show that partial blockage of GABA A receptors by gabazine (GBZ) application (10 M, a concentration that promotes expansion of cortical receptive fields) increased the evoked firing rate and the spike-timing reliability during presentation of communication sounds (conspecific and heterospecific vocalizations), whereas GABA B receptor antagonists [10 M saclofen; 10 -50 M CGP55845 (p-3-aminopropyl-p-diethoxymethyl phosphoric acid)] had nonsignificant effects. Computing mutual information (MI) from the responses to vocalizations using either the evoked firing rate or the temporal spike patterns revealed that GBZ application increased the MI derived from the activity of single cortical site but did not change the MI derived from population activity. In addition, quantification of information redundancy showed that GBZ significantly increased redundancy at the population level. This result suggests that a potential role of intracortical inhibition is to reduce information redundancy during the processing of natural stimuli.

Inhibition does not affect the timing code for vocalizations in the mouse auditory midbrain

“…In contrast, we did not observe the increase of spike timing reliability reported in Gaucher et al (2013). This may indicate different processing strategies between the IC and auditory cortex, but it may also be a consequence of the different pharmacological protocols.…”

Representation of individual elements of a complex call sequence in primary auditory cortex

“…These could then be processed by intrinsic circuits in the cortex, involving inhibition of selected elements (Gaucher et al, 2013a), or convergence of different thalamic inputs in order to produce a much greater range of cortical response combinations. Some of our orthogonal tracks were consistent with the idea of convergence from different thalamic inputs and inhibition is undoubtedly involved, but overall the variety of chutter response combinations identified in the thalamus appeared to be just as great as in the cortex.…”

“…This theory is based on the expectation that there is a relatively small degree of redundancy within cortical responses and so finding large numbers of identical responses to a call sequence would be inconsistent with this view. Recent work has shown that at the level of individual neurons there is relatively little redundancy in the response to conspecific calls such as chutter partly as a result of local inhibition within AI (Gaucher et al, 2013a). Our results were consistent with other work showing that the temporal sparse code found in AI is a first step in generating a high level representation of conspecific vocalizations (Gaucher et al, 2013b).…”

Neural correlates of moderate hearing loss: time course of response changes in the primary auditory cortex of awake guinea-pigs

“…Since the three tested vocalizations had different lengths (from 90 ms for the “chirp,” up to 1740 ms for the “chutter”), only the first 90 ms were analyzed to allow pooling of the responses to different vocalizations. The spike timing reliability was computed using the CorrCoef as in previous studies (Gaucher et al, 2013a). It corresponds to the normalized covariance between each pair of spike trains recorded at presentation of this vocalization and was calculated as follows: $CorrCoef=\frac{1}{N(N-1)}{\displaystyle \sum _{i=1}^{N-1}{\displaystyle \sum _{j=i+1}^N\frac{\sigma x_i{x}_j}{\sigma x_i\sigma x_j}}}$ where N is the number of trials and σ x i x j is the normalized covariance at zero lag between spike trains x i and x j where i and j are the trial numbers.…”