Go/No-Go task engagement enhances population representation of target stimuli in primary auditory cortex

Abstract

Primary sensory cortices are classically considered to extract and represent stimulus features, while association and higher-order areas are thought to carry information about stimulus meaning. Here we show that this information can in fact be found in the neuronal population code of the primary auditory cortex (A1). A1 activity was recorded in awake ferrets while they either passively listened or actively discriminated stimuli in a range of Go/No-Go paradigms, with different sounds and reinforcements. Population-level dimensionality reduction techniques reveal that task engagement induces a shift in stimulus encoding from a sensory to a behaviorally driven representation that specifically enhances the target stimulus in all paradigms. This shift partly relies on task-engagement-induced changes in spontaneous activity. Altogether, we show that A1 population activity bears strong similarities to frontal cortex responses. These findings indicate that primary sensory cortices implement a crucial change in the structure of population activity to extract task-relevant information during behavior.

Network dynamics underlying OFF responses in the auditory cortex

“…Across different stimuli, OFF responses were elicited in overlapping subsets of neurons, where individual neurons responded to more than one stimulus. Analyses of the population activity through dimensionality reduction techniques (Cunningham and Yu, 2014;Kobak et al, 2016;Bagur et al, 2018;Stringer et al, 2019) revealed that OFF responses encode different stimuli in orthogonal low-dimensional subspaces, consistent with the existence of a neural code for OFF responses at the ensemble level (Saha et al, 2017). We interpret these results by examining a linear network model with recurrent connectivity (Bondanelli and Ostojic, 2018), and show that it provides a simple mechanism for the generation of the strong single-cell OFF responses observed in data from auditory cortex.…”

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

“…Importantly, we validate our inference methods for all of these features using ground truth in-vitro and in-vivo data from whole-cell [49,50] and combined juxtacellular-extracellular recordings [51]. Systematic comparisons with existing model-based and model-free methods on synthetic data and electrophysiological recordings [49][50][51][52] reveal a number of advantages, in particular for the challenging task of estimating synaptic couplings from highly subsampled networks.…”

An emergent population code in primary auditory cortex supports selective attention to spectral and temporal sound features

“…In the last several years, multiple studies of A1 have suggested the importance of population-level representations. Studies have found that population-level analyses provide clear links between A1 activity and behavior, whereas single neurons may not (Bagur et al, 2018;Christison-Lagay et al, 2017;See et al, 2018;Yao and Sanes, 2018). A critical question has remained unanswered, however: To what extent are these population-level findings an expected by-product of marginalizing across neurons (Sasaki et al, 2017)?…”

“…Thus, a widely held view is that A1 and other PSCs are best described as arrays of adaptive sensory filters, whereby simple feature tuning is modulated by ongoing behavioral and sensory demands. However, recent studies show that A1 neurons' synergistic interactions can play a large role in sensory processing -a role which often cannot be understood solely based on the activity of individual neurons (Bagur et al, 2018;Bathellier et al, 2012;Harris et al, 2011;See et al, 2018). Rather, individual neurons' activity may be better understood in terms of their contributions within functional ensembles.…”