Hierarchical differences in population coding within auditory cortex

Abstract

Most models of auditory cortical (AC) population coding have focused on primary auditory cortex (A1). Thus our understanding of how neural coding for sounds progresses along the cortical hierarchy remains obscure. To illuminate this, we recorded from two AC fields: A1 and middle lateral belt (ML) of rhesus macaques. We presented amplitude-modulated (AM) noise during both passive listening and while the animals performed an AM detection task ("active" condition). In both fields, neurons exhibit monotonic AM-depth tuning, with A1 neurons mostly exhibiting increasing rate-depth functions and ML neurons approximately evenly distributed between increasing and decreasing functions. We measured noise correlation (r_noise) between simultaneously recorded neurons and found that whereas engagement decreased average r_noise in A1, engagement increased average r_noise in ML. This finding surprised us, because attentive states are commonly reported to decrease average r_noise We analyzed the effect of r_noise on AM coding in both A1 and ML and found that whereas engagement-related shifts in r_noise in A1 enhance AM coding, r_noise shifts in ML have little effect. These results imply that the effect of r_noise differs between sensory areas, based on the distribution of tuning properties among the neurons within each population. A possible explanation of this is that higher areas need to encode nonsensory variables (e.g., attention, choice, and motor preparation), which impart common noise, thus increasing r_noise Therefore, the hierarchical emergence of r_noise-robust population coding (e.g., as we observed in ML) enhances the ability of sensory cortex to integrate cognitive and sensory information without a loss of sensory fidelity.NEW & NOTEWORTHY Prevailing models of population coding of sensory information are based on a limited subset of neural structures. An important and under-explored question in neuroscience is how distinct areas of sensory cortex differ in their population coding strategies. In this study, we compared population coding between primary and secondary auditory cortex. Our findings demonstrate striking differences between the two areas and highlight the importance of considering the diversity of neural structures as we develop models of population coding.

Keywords: amplitude modulation; attention; auditory cortex; belt; noise correlation.

Fig. 1.

Different AM tuning properties in A1 and ML lead to different r_tuning distributions. A, left: shown are the firing rates along the range of tested AM depths for 2 simultaneously recorded ML neurons, with their rate-depth slopes noted at right. Right panel shows their joint mean firing rate distribution and the r_tuning value between them. This value is negative because as the mean firing rate of neuron 1 decreases, the mean firing rate of neuron 2 increases. B: same as in A but for a pair with positive r_tuning. C and D: the distribution of slopes in ML has 2 peaks, corresponding to distinct populations of neurons with increasing and decreasing rate-depth slopes, respectively, whereas the distribution in A1 has 1 peak and favors increasing slopes. E and F: the r_tuning of simultaneously recorded pairs in ML and A1 (simultaneous r_tuning) reveals a greater proportion of pairs with negative r_tuning in ML. G and H: r_tuning is calculated between all recorded neurons (all cells r_tuning), showing that the r_tuning distribution has 2 peaks in ML and 1 positive peak in A1. Error bars are SE.

Fig. 2.

Task engagement differentially affects r_noise in ML and A1. A and B: during task engagement (active; open bars), r_noise on average goes up in ML (A) but goes down in A1 (B). These effects, in both A1 and ML, hold primarily for pairs with positive r_tuning (C and D), whereas task engagement does not significantly impact r_noise in pairs with negative r_tuning (E and F). Inset histograms show the distribution of r_noise values across the pairs used for each comparison. Error bars are SE.

Fig. 3.

In both ML (A) and A1 (B), pairs classify sounds better in the active condition (open circles) relative to the passive condition (filled squares).

Fig. 4.

Classifier performance in 3 example pairs over a range of simulated r_noise values. r_noise was varied from −1 to 1 measure classifier performance for each simulation. The pairs in A and C show striking nonmonotonicity in their relationships between r_noise and classifier performance. The pair in B, on the other hand, shows a monotonic relationship. Gray vertical lines show r_noise at maximum performance, and black vertical lines show r_noise at minimum performance.

Fig. 5.

Distributions of r_noise values corresponding to pairs’ best (max) and worst (min) classifier performance in ML and A1. For pairs that violate the SR, r_noise at maximum performance will be at a value that is the same sign as the r_tuning value, and/or r_noise at minimum will have the opposite sign as r_tuning. These distributions are not distinct between ML and A1, suggesting that pairwise violations of the SR do not contribute to a different role for r_noise in sensory coding between ML and A1. Dark shaded bars indicate pairs that violate the SR.

Fig. 6.

Task-related changes in r_noise enhance coding in A1, not in ML. Values above 0 indicate that r_noise in the active condition uniquely brings about an improvement in pairwise performance. r_noise changes in ML (A) do not uniquely contribute to the observed increase in pairwise performance shown in Fig. 3. However, in A1 (B), r_noise does make a contribution to increased coding accuracy. In ML (C and E), task-related r_noise effects do not contribute to average performance differences in either the positive (C) or negative (E) r_tuning groups. However, at AM depths of 60 and 80%, r_noise contributes to decreased (C) or increased (E) performance, in pairs with positive or negative r_tuning, respectively. In A1 (D and F), task-related r_noise effects appear to enhance sensory coding across both r_tuning categories.

Fig. 7.

In both ML and A1, r_tuning and r_noise are positively correlated. We incorporate this relationship in our simulations of r_noise across modeled neural populations. Note that each pair contributes 8 points to the plot (1 point per stimulus condition).

Fig. 8.

In both ML and A1, pairwise firing rate and r_noise are negatively correlated. As in Fig. 7, each pair contributes 8 points (1 per stimulus condition) to this plot. Firing rates are z-scored within each pair, across stimulus conditions. We also incorporate this relationship in our simulations of r_noise across modeled neural populations.

Fig. 9.

ML and A1 modeled populations are differentially affected by r_noise. A: an example neuron’s normalized firing rate and slope. B and C: 2 example instantiations of modeled populations of size n = 100 neurons. Populations are modeled as arrays of tuned filters, with neurons arranged according to AM slope (from most negative to most positive). Population mean responses to each stimulus are fit with 3rd-order polynomial least-squares curves (solid lines). Each data point represent the mean (maximum normalized) response of one neuron to one stimulus (black for 0% AM and gray for 100% AM). D and E: in populations of n = 100 neurons, across 400 simulated trials per simulated r_noise, average classifier performance was obtained at each AM depth in ML and A1. Whereas ML is modestly affected by r_noise, and only at higher AM depths, A1 performance is dramatically reduced for weakly positive r_noise values at each depth other than 6%.