Perceptual learning directs auditory cortical map reorganization through top-down influences

Abstract

The primary sensory cortex is positioned at a confluence of bottom-up dedicated sensory inputs and top-down inputs related to higher-order sensory features, attentional state, and behavioral reinforcement. We tested whether topographic map plasticity in the adult primary auditory cortex and a secondary auditory area, the suprarhinal auditory field, was controlled by the statistics of bottom-up sensory inputs or by top-down task-dependent influences. Rats were trained to attend to independent parameters, either frequency or intensity, within an identical set of auditory stimuli, allowing us to vary task demands while holding the bottom-up sensory inputs constant. We observed a clear double-dissociation in map plasticity in both cortical fields. Rats trained to attend to frequency cues exhibited an expanded representation of the target frequency range within the tonotopic map but no change in sound intensity encoding compared with controls. Rats trained to attend to intensity cues expressed an increased proportion of nonmonotonic intensity response profiles preferentially tuned to the target intensity range but no change in tonotopic map organization relative to controls. The degree of topographic map plasticity within the task-relevant stimulus dimension was correlated with the degree of perceptual learning for rats in both tasks. These data suggest that enduring receptive field plasticity in the adult auditory cortex may be shaped by task-specific top-down inputs that interact with bottom-up sensory inputs and reinforcement-based neuromodulator release. Top-down inputs might confer the selectivity necessary to modify a single feature representation without affecting other spatially organized feature representations embedded within the same neural circuitry.

Figures

Figure 1.

Stimulus statistics and behavioral performance for the auditory recognition task. A, B, Distribution of tone frequencies and intensities presented in training days 1–2 and all post-asymptotic training sessions in FR (A) and LR (B) rats. Any tone falling along the dashed horizontal line served as a target stimulus for the LR task. Any tone falling along the solid vertical line was a target stimulus for the FR task. C, D, Frequencies (C) and intensities (D) presented in each behavioral trial are compared between FR (red) and LR (black) rats on days 1–2 (squares) and post-asymptotic (PA) days (circles). Tone frequency categories (C) are ¾-octave-wide bins centered on the frequency value shown. E, F, Behavioral performance as a function of tone frequency (E) and tone intensity (F) during the post-asymptotic training sessions for all rats trained in the FR (red) and LR (black) tasks. All values shown are mean ± SE.

Figure 2.

Documentation of perceptual learning in the auditory recognition task. A, The highest training phase reached on an individual day of training is shown across the entire training period for one rat in the FR task categorized as a rapid learner (dashed gray line) and one rat in the LR task classified as a slow learner (solid black line). The description of auditory stimuli used in each phase is provided in Table 1. B, Task performance for the same rats shown in A obtained from a single day of training (indicated by black and gray arrows in A) is shown to illustrate the adaptive tracking protocol. C, D, Fifty percent recognition thresholds plotted across each training day for the same rapid learning rat (C) and slow learning rat (D). Changes in the recognition threshold between the first day of training until performance reached an asymptotic level were fit with a linear regression (thick gray line), and the slope of the regression line was defined as the slope of the learning curve. Dashed lines indicate post-asymptotic training days that were not included in the slope calculation. E, F, Target recognition threshold values (mean ± SE) across the first 28 d of training for all rats in the FR (E) and LR (F) tasks.

Figure 3.

Task-specific reorganization of cortical maps in the frequency domain. A, B, Representative tonotopic maps from AI (A) and SRAF (B) were delineated with fine-grain microelectrode mapping. The color of each polygon in the tessellated map represents the CF associated with neurons located in the middle cortical layers at that position in the map. Gray shaded polygons indicate recording sites with CF values within the trained frequency range (5 kHz ± 0.375 octaves). Filled circles indicate unresponsive sites. Open circles represent sites with sound-driven responses that did not meet the criteria for inclusion in AI or SRAF. Scale bar, 1 mm. The arrows indicate dorsal (D) and anterior (A) orientations. C, D, Distribution of CF values in AI (C) and SRAF (D) for all recordings obtained in control (green), FR-trained (red), and LR-trained (black) rats. Dashed lines indicate the trained frequency range. E, Difference functions were calculated by subtracting the CF distribution in control rats from FR-trained (solid red) and LR-trained (dashed black) CF distributions. Zero values (solid black line) indicate no difference relative to controls. Tone frequency categories (C–E) are ¾-octave-wide bins centered on the frequency value shown. F, Mean ± SE percentage of map area with CF values in the trained frequency range (TFR). The asterisk indicates a statistically significant difference obtained with an unpaired t test (p < 0.05).

Figure 4.

Task-specific reorganization of cortical maps in the intensity domain. A, B, Representation of sound intensity in AI (A) and SRAF (B) are created using the same conventions as in Figure 2, except that the color within each polygon indicates the best level for neurons at that recording site, rather than CF. Empty polygons indicate recording sites where a best level could not be determined because responses were poorly driven by white-noise bursts. Blue shaded polygons indicate recording sites with best-level values in the trained intensity rage (35 ± 5 dB SPL). The arrows indicate dorsal (D) and anterior (A) orientations. C, D, Distribution of best-level values in AI (C) and SRAF (D). The dotted line indicates the trained intensity range. Each distribution is fit with a fourth-degree polynomial function (colored lines) to illustrate the shape of the distribution. E, Difference functions were calculated by subtracting the best-level distribution values in control rats from the best-level distribution values in LR-trained (left) and FR-trained (right) rats. Solid and dashed lines represent linear fits of the difference values for AI (solid black) and SRAF (dashed gray). Flat slopes indicate no difference relative to control. F, Mean ± SE percentage of map area tuned to the trained intensity range (TIR) in control (green), FR-trained (red), and LR-trained (black) rats. The asterisk indicates a statistically significant difference obtained with an unpaired t test (p < 0.05).

Figure 5.

Plasticity in cortical recruitment functions. A–D, Mean ± SE percentage of the cortical map area activated by tones grouped into seven frequency categories in control (A, C) and LR (B, D) rats for AI (A, B) and SRAF (C, D). E, F, Difference functions (LR minus control) for all frequencies in AI (E) and SRAF (F). Zero values (solid black lines) indicate no difference relative to controls.

Figure 6.

The percentage of recording sites tuned to the task-relevant stimulus feature is correlated with the degree of perceptual learning. A, Percentage of AI and SRAF recording sites tuned to the trained frequency range (TFR) in each FR-trained rat. B, Percentage of AI and SRAF recording sites tuned to the trained intensity range (TIR) in each LR-trained rat. The linear fit for each data set is represented by the dashed line. The upward triangle data point (A) and downward triangle data point (B) correspond to the rapid-learning FR rat and slow-learning LR rat, respectively, in Figure 2A–D. Solid gray lines represents the mean percentage of recording sites tuned to the TFR (A) and TIR (B) in control rats.

Figure 7.

Task-specific restructuring of receptive fields for sound intensity. A, Example raster plots illustrate three representative types of neural responses to white-noise bursts of varying intensity: monotonically increasing (type A), saturating (type B), and nonmonotonic (type C). Each dot represents the occurrence of an action potential. B, RLFs correspond to the raster plots shown in A. The minimum response threshold, transition point, and response at highest intensity are indicated by the arrows, diamonds, and squares, respectively. C, Frequency of occurrence for type A, B, and C RLFs in AI and SRAF for control (green), FR (red), and LR (black) rats. The color scheme applies for all subsequent panels. D, Mean ± SE slope of RLF rising phase (between the arrow and diamond in B). The asterisks indicate statistically significant differences obtained with an unpaired t test (p < 0.05). E, Cumulative percentage plots illustrate shifts in the distribution of monotonicity values (slope of the RLF between the diamond and square in B) in AI (left) and SRAF (right).

Figure 8.

Task-specific changes in tuning curve bandwidth. A, B, Q-factor (mean ± SE) measured 42 dB above threshold for recording sites in control (open bars), FR (diagonal hatched bars), and LR (crosshatched bars) rats for AI (A) and SRAF (B). Higher Q42 values represent narrower tuning curve bandwidths. The asterisks indicate statistically significant differences compared with control values (p < 0.025). C, D, The same data are represented as difference functions (trained rat values minus control values) to highlight changes between FR versus control (C) rats and LR versus control (D) rats in AI (solid black lines) and SRAF (dashed gray lines). Zero values (solid black lines in C and D) indicate no difference relative to controls. Tone frequency categories are ¾-octave-wide bins centered on the frequency value shown.

Figure 9.

Cortical map reorganization best explained by a model limited to task-relevant stimulus features. Line plots depict differences in the CF distributions (A) and best-level distributions (B) in FR-trained rats (solid lines) and LR-trained rats (dashed lines) relative to controls (value = trained − control). The first three rows present predicted changes in CF and best-level distributions based on overall stimulus statistics encountered in the task (top row), the stimulus statistics that precede rewarded trials only (second row), and stimulus statistics limited to the attended stimulus dimension (third row). The predicted changes for the FR and LR groups are compared with the actual changes in CF and best-level distributions in the bottom row. The CF and best-level categories are identical to those used in Figure 1. The actual data shown in the bottom reflect mean values pooled from AI and SRAF (shown individually in Figs. 3c–e, 4c–e). Max, Maximum; Min, minimum.