Temporally precise population coding of dynamic sounds by auditory cortex

Abstract

Fluctuations in the amplitude envelope of complex sounds provide critical cues for hearing, particularly for speech and animal vocalizations. Responses to amplitude modulation (AM) in the ascending auditory pathway have chiefly been described for single neurons. How neural populations might collectively encode and represent information about AM remains poorly characterized, even in primary auditory cortex (A1). We modeled population responses to AM based on data recorded from A1 neurons in awake squirrel monkeys and evaluated how accurately single trial responses to modulation frequencies from 4 to 512 Hz could be decoded as functions of population size, composition, and correlation structure. We found that a population-based decoding model that simulated convergent, equally weighted inputs was highly accurate and remarkably robust to the inclusion of neurons that were individually poor decoders. By contrast, average rate codes based on convergence performed poorly; effective decoding using average rates was only possible when the responses of individual neurons were segregated, as in classical population decoding models using labeled lines. The relative effectiveness of dynamic rate coding in auditory cortex was explained by shared modulation phase preferences among cortical neurons, despite heterogeneity in rate-based modulation frequency tuning. Our results indicate significant population-based synchrony in primary auditory cortex and suggest that robust population coding of the sound envelope information present in animal vocalizations and speech can be reliably achieved even with indiscriminate pooling of cortical responses. These findings highlight the importance of firing rate dynamics in population-based sensory coding.

Psychometric and subcortical neurometric measures of temporal discrimination in rhesus macaques

“…Some more recent studies of primate auditory cortex have included the onset response, which facilitates comparison to the current results. These studies also used a modulation frequency decoding framework, further aiding comparison to the present results (Downer et al, 2021;Hoglen et al, 2018). Hoglen et al (2018) report that decoding of modulation frequency exhibited similar patterns across 0-750 ms and 250-1000 ms time-windows, which may indicate that the onset response does not alter results in the present report, which is consistent with the remarks in Johnson et al (2012).…”

“…These differences from previous CN and IC studies must be taken with a caveat: the onset response has been excluded in many previous studies (Beitel et al, 2003; Henry et al, 2016; Johnson et al, 2012; Sayles et al, 2013), though, interestingly, Johnson et al (2012) report that “including the onset response did not substantially alter the results.” Some more recent studies of primate auditory cortex have included the onset response, which facilitates comparison to the current results. These studies also used a modulation frequency decoding framework, further aiding comparison to the present results (Downer et al, 2021; Hoglen et al, 2018). Hoglen et al (2018) report that decoding of modulation frequency exhibited similar patterns across 0-750 ms and 250-1000 ms time-windows, which may indicate that the onset response does not alter results in the present report, which is consistent with the remarks in Johnson et al (2012).…”

“…Consistent with this, the average neuronal sensitivity based on spike-timing in the cochlear nucleus (CN) and inferior colliculus (IC) correlated with AM detection performance (Henry et al, 2016; Sayles et al, 2013), while the average rate of simulated populations in auditory cortex (A1) correlated with AM detection performance (Johnson et al, 2012). Such population analyses have revealed that the decrease in spike-timing precision in auditory cortex (A1) may be recovered at the population level (Downer et al, 2021; Johnson et al, 2012), and can test predictions of modulation filter-bank models (Dau et al, 1997; Maxwell et al, 2020; Verhulst et al, 2018; Viswanathan et al, 2022), which factor heavily in current thinking of auditory perception.…”

Parvalbumin neurons, temporal coding, and cortical noise in complex scene analysis

“…Previous studies have demonstrated that neurons with the highest performance are most strongly correlated with behavior and strongly constrain population performance 28, 29, 30, 31, 32 . Thus, we were curious to test how the performance of the best neurons in our population would be affected by optogenetic suppression of PV neurons.…”

A Robust, Compact and Diverse Population Code for Competing Sounds in Auditory Cortex

“…Recent studies in ACx have investigated population coding of natural sounds (Ince et al, 2013), and dynamic amplitude modulated sounds (Downer et al, 2021). However, population coding of competing sounds has yet to be investigated, motivating the following questions: Can population coding improve the representation of competing sounds?…”