Gain Control in the Auditory Cortex Evoked by Changing Temporal Correlation of Sounds

Abstract

Natural sounds exhibit statistical variation in their spectrotemporal structure. This variation is central to identification of unique environmental sounds and to vocal communication. Using limited resources, the auditory system must create a faithful representation of sounds across the full range of variation in temporal statistics. Imaging studies in humans demonstrated that the auditory cortex is sensitive to temporal correlations. However, the mechanisms by which the auditory cortex represents the spectrotemporal structure of sounds and how neuronal activity adjusts to vastly different statistics remain poorly understood. In this study, we recorded responses of neurons in the primary auditory cortex of awake rats to sounds with systematically varied temporal correlation, to determine whether and how this feature alters sound encoding. Neuronal responses adapted to changing stimulus temporal correlation. This adaptation was mediated by a change in the firing rate gain of neuronal responses rather than their spectrotemporal properties. This gain adaptation allowed neurons to maintain similar firing rates across stimuli with different statistics, preserving their ability to efficiently encode temporal modulation. This dynamic gain control mechanism may underlie comprehension of vocalizations and other natural sounds under different contexts, subject to distortions in temporal correlation structure via stretching or compression.

Keywords: adaptation; auditory cortex; electrophysiology; gain control; natural sounds.

Figure 1.

Recording neuronal spiking activity from primary auditory cortex (A1). (A) Reconstruction of primary auditory cortex showing tetrode traces in black dashed lines and cortical area borders in white lines. (B) Distribution of the best frequency and bandwidth of recorded units. (C) Top row: 100 ms sample of the amplitude envelope across each frequency for each stimulus TC level. Below, waveforms of the repeated 10 s stimuli, from which each sample is extracted. Center row: Spike raster from a single neuron in response to 50 repeats of each stimulus TC level. Bottom row: Mean firing rate PSTH of response to each stimulus TC level. Left column: low TC. Center column: medium TC. Right column: high TC.

Figure 2.

Properties of neuronal spiking in response to varied TC levels. (A) Top: 8 s sample of the stimulus amplitude envelope for the alternating low-to-high TC stimulus. Transitions between TC levels occurred every 2 s (black dashed lines). (B) Mean firing rate PSTHs from 3 representative neurons aligned to the TC level transitions every 8 s. (C) STRFs from Neuron 3 in response to low, medium, and high TC levels.

Figure 3.

Predicted increase in neuronal responses with increased stimulus TC. (A) Linear–nonlinear model diagram illustrating how the model predicts the firing rate in response to input stimulus: Amplitude modulation envelope of the stimulus is convolved with the linear filter (STRF) to produce the linear output, (Equation 2), which is subject to a transfer function (exponential fit to the nonlinearity) to generate the predicted firing rate for the neuron (Equation 3). (B) Sample STRF. (C) Sample nonlinearity (red: exponential fit, black: data). (D,E) Model predictions for responses to low or high stimulus TC levels. Left panels: Example of model outputs fitted to a single neuron's TC stimulus response properties. The red box in the model diagram highlights the feature being analyzed. Middle: Single neuron responses. Right: Population histogram of the change in predicted response with increased stimulus TC. (D) Standard deviation of the linear output (SD_LO, Equation 2) of the low TC model in response to low or high TC stimuli. (E) Predicted mean firing rate (top) and standard deviation (bottom) (Equation 3) of the low TC model in response to low versus high TC stimuli. Fit to low TC responses: black; fit to high TC responses: gray. Here and below: unity line: gray dashed.

Figure 4.

Adaptation in neuronal responses to stimuli with increased temporal correlation. (A) Stimulus amplitude envelope, as in Figure 1C, for the alternating high–low TC stimulus. (B) Mean neuronal firing rate to high TC versus low TC stimulus. Left: single neuron responses, right: histogram of population responses (blue: significant decrease, red: significant increase; white: not significant). (C) Standard deviation of the firing rate to high TC versus low TC stimulus. Panels same as in (B). (D) Actual versus predicted change in the mean firing rate. Left: prediction based on standard deviation of the linear output. Right: prediction based on full linear–nonlinear model. (E) Actual versus predicted change in standard deviation of the firing rate. Panels same as in D.

Figure 5.

Gain adaptation in neuronal responses to stimuli with increased temporal correlation. (A) Exponential nonlinearity fitted to the actual firing rate response to low versus high TC stimuli. Gain is measured through a linear fit to the exponential. Cyan: low TC fit, Magenta: high TC fit. Responses to low TC stimulus: black circles; responses to high TC stimulus: gray circles. (B) Gain measurements for high versus low TC stimuli. Left: individual neurons, right: histogram of change in the gain. Stars indicate that gain was higher for low TC than for high TC stimuli (left panel) and that gain decreased upon transition from high to low TC stimuli (right panel). (C) Change in the gain versus the change in the firing rate (left) or the standard deviation of the firing rate (right). (D, E, F) Same as in (A), (B), (C) but with logistic nonlinearity fit. Gain is measured as the steepness parameter k in Equation 5. (G) Predictions for the firing rate based on models fitted to high versus low TC stimulus. Left: individual neurons. Center: histogram of the index of change of the predicted firing rate with increasing TC. Right: actual versus predicted change in firing rate. (H) Predictions for the standard deviation of the firing rate based on models fitted to high versus low TC stimulus. Panels same as in G.

Figure 6.

Neuronal firing rates in response to intermediate TC-level changes. (A) Transition from low to medium TC. (B) Transition from medium to high TC. (A, B) Left: Stimulus envelope, as in Figure 5A. Right: Change in mean firing rate (top) and standard deviation of the firing rate (bottom) from low to high TC stimulus. Axes and colors same as in Figure 4B,C. (C) Correlation between change in mean firing rate for medium-to-low and high-to-medium TC stimuli.

Figure 7.

Neuronal spectrotemporal receptive fields remain stable across varying temporal correlation levels. (A) Spectrotemporal receptive field (STRF) of a neuron in response to low (left), medium (middle), and high (right) stimulus TC levels. Excitatory lobe: white, inhibitory lobe: black. Excitatory lobe bandwidth: vertical line. Excitatory lobe duration: horizontal line. The intersection of the black lines marks the excitatory lobe center frequency and time to peak. (B–E) Analysis of model parameters (positive lobe of the linear filter) in response to high-to-low stimulus TC levels for each neuron. (B) Frequency bandwidth. (C) Center frequency. (D) Duration. (E) Time to peak. (B–E) Left: Single neuron data. Center: Population histogram. Right: Correlation between the change in the STRF parameter versus the change in the firing rate with increased TC.

Figure 8.

Improved encoding efficiency with increases in temporal correlation. (A) Fano factor of each neuron for low versus high stimulus TC. (B) Signal-to-noise ratio of each neuron for low versus high stimulus TC. (C) Prediction quality of each neuron for low versus medium stimulus TC. (D) Prediction quality of each neuron for medium versus high stimulus TC. Left, middle, and right panels as in Figure 6B–D. (E) Index of change in prediction quality from low-to-medium stimulus TC versus medium-to-high stimulus TC for each neuron. Plot axes as in D, left right panel.

Figure 9.

Heterogeneous responses to abrupt changes in stimulus TC. (A–D) Example PSTHs of the average firing rate of neurons with the transition from one TC level to another centered at time 0. Transitions from high to low TC and its adaptation fit (decaying exponential function, Equation 6) are in orange and dashed dark orange lines, respectively. Transitions from low to high TC and its adaptation fit are in green and dashed dark green lines, respectively. (A) A neuron that displays a peak in firing rate after either transition. (B) A neuron that displays a dip in firing rate after either transition. (C) A neuron that displays a dip in firing rate after transition to low TC and a peak in firing rate after transition to high TC. (D) A neuron that displays a peak in firing rate after transition to low TC and a dip in firing rate after transition to high TC. (E) Z-score of the initial response (k) after transition to low versus high TC. Each neuron is represented by a circle and its fill color indicates the index of change in adapted firing rate (c). (F) Time constant (τ) of the firing rate adaptation after the initial response to the low versus to the high stimulus TC. Each neuron is represented as in E.