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 () between simultaneously recorded neurons and found that whereas engagement decreased average in A1, engagement increased average in ML. This finding surprised us, because attentive states are commonly reported to decrease average We analyzed the effect of on AM coding in both A1 and ML and found that whereas engagement-related shifts in in A1 enhance AM coding, shifts in ML have little effect. These results imply that the effect of 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 Therefore, the hierarchical emergence of -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. 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.

Contribution of spiking activity in the primary auditory cortex to detection in noise

“…It is not clear, though, whether the brain reads out all neurons equivalently. Indeed, it may very well be that downstream neurons only read out the most informative neurons (Ince et al 2013;Law and Gold 2008), for example, those neurons whose firing rate was modulated to the greatest extent (and/or most reliably) by different values of TNR or only those neurons whose tuning properties matched the frequency of the target stimulus (Downer et al 2017;Jazayeri and Movshon 2006;Miller and Recanzone 2009;Yang and Lisberger 2009). Second, the degree to which access to larger populations of A1 neurons affects decoding cannot be readily determined without knowing the exact process by which A1 activity is read out.…”

“…As a consequence of this random selection, we minimized the contribution of correlation structure on the population read out, a practice consistent with several previous studies (Pagan et al 2013;DiCarlo 2010, 2012). Finally, similarly to previous studies (Downer et al 2017;Jazayeri and Movshon 2006;Miller and Recanzone 2009;Yang and Lisberger 2009), we set the frequency of the target stimulus at each neuron's best frequency. As a consequence, our population of A1 neurons was recorded using different stimuli.…”

Psychometric and subcortical neurometric measures of temporal discrimination in rhesus macaques

“…Neuronal responses to AM have been well characterized along the auditory pathway (Bartlett and Wang, 2007;Beitel et al, 2003Beitel et al, , 2020Bendor and Wang, 2008;Downer et al, 2017;Joris et al, 2004;Langner and Schreiner, 1988;Nelson and Carney, 2007;Niwa et al, 2013;Rhode et al, 2010;Sayles et al, 2013;Wang et al, 2008). In early stages (e.g.…”

An Emergent Population Code in Primary Auditory Cortex Supports Selective Attention to Spectral and Temporal Sound Features

“…Multiple recent studies of A1 highlight the importance of population-level representations (Downer et al, 2015(Downer et al, , 2017aPachitariu et al, 2015;Francis et al, 2018). Population-level analyses can provide clear links between A1 activity and behavior, whereas single neurons may not (Christison-Lagay et al, 2017;Bagur et al, 2018;See et al, 2018;Yao and Sanes, 2018;Sadeghi et al, 2019).…”

“…Thus, our simulated populations contained realistically structured spike count variability. The methods for introducing realistic noise correlations and Fano factor in simulated neural populations are detailed by Shadlen and Newsome (1998) and Downer et al (2017a). All simulated population results presented in this manuscript are from simulations in which we impose noise correlations between pairs of neurons as described above.…”