Top-down modulation of sensory cortex gates perceptual learning

Abstract

Practice sharpens our perceptual judgments, a process known as perceptual learning. Although several brain regions and neural mechanisms have been proposed to support perceptual learning, formal tests of causality are lacking. Furthermore, the temporal relationship between neural and behavioral plasticity remains uncertain. To address these issues, we recorded the activity of auditory cortical neurons as gerbils trained on a sound detection task. Training led to improvements in cortical and behavioral sensitivity that were closely matched in terms of magnitude and time course. Surprisingly, the degree of neural improvement was behaviorally gated. During task performance, cortical improvements were large and predicted behavioral outcomes. In contrast, during nontask listening sessions, cortical improvements were weak and uncorrelated with perceptual performance. Targeted reduction of auditory cortical activity during training diminished perceptual learning while leaving psychometric performance largely unaffected. Collectively, our findings suggest that training facilitates perceptual learning by strengthening both bottom-up sensory encoding and top-down modulation of auditory cortex.broad range of sensory skills improve with practice during perceptual learning (PL), including language acquisition (1-3), musical abilities (4), and recognition of emotions (5). The neural bases for such perceptual improvement may vary widely. For example, training-based changes in neural activity have been identified in a number of brain regions, including early (6-13) and late (14, 15) sensory cortices, multisensory regions (16, 17), and downstream decision-making areas (18). Similarly, several neural mechanisms have been proposed, such as enhanced signal representation (19), reduction of external (20, 21) or internal (22, 23) noise, and improvement in sensory readout or decision making (13,18,24,25).The apparent divergence of loci and mechanisms associated with PL could be due, in part, to limitations of experimental design. For example, some neural changes associated with PL are transient (26-29), making it necessary to monitor neural activity throughout the duration of perceptual training, rather than making comparisons only after PL is complete (6-12, 14-16, 30). For similar reasons, it is critical to block the function of a specific candidate brain region during training to determine whether it plays a causal role in PL. Although some reports show that manipulating brain activity can influence PL (28,(31)(32)(33)(34), there are no loss-of-function experiments to determine whether a particular region is required for behavioral improvement.To address these unresolved issues, we recorded from auditory cortex (ACx) as animals improved on an auditory detection task and, in separate experiments, blocked ACx activity during the period of perceptual training. We found that neural and behavioral sensitivity improved in a nearly identical manner over the course of training, in terms of both absolute magnitude and kinetics. Furthermore,...

Tracing the Trajectory of Sensory Plasticity across Different Stages of Speech Learning in Adulthood

“…We further provide evidence for training-induced neurophysiological changes in sensory encoding that relate to behavioral stability, and endure beyond a period of intensive training. Our findings are in support of an emerging view that auditory perceptual learning is mediated by top-down processes that shape sensory signals through later learning phases [30]. …”

“…In line with this idea, animal models have revealed top-down gated sensory enhancement of selective features of incoming acoustic stimuli following extensive behaviorally-relevant training [26–28]. Our findings suggest that native listeners and over-trained learners may be able to operate at the level of category-based perception and reach down to lower-levels of the sensory hierarchy for tuning of behaviorally-relevant signals, depending on the nature of the task [29, 30]. Learners who demonstrated better perceptual identification of tone categories (thought to reflect higher-level attention-driven processes [31]) at the novice learning phase, took fewer days to reach the learning criterion (Figure 3C).…”

Task rule and choice are reflected by layer-specific processing in rodent auditory cortical microcircuits

“…Therefore, we propose that the representation of stimulus features in sensory cortex, such as tone frequency in A1, does not depend alone on the transmission process of the sensory information via the primary sensory pathways, but is significantly modulated by the behavioral need and the behavioral relevance of a stimulus. Such influence is based presumably on higher order top-down inputs from, for instance, parietal and frontal areas (Caras and Sanes, 2017;Polley et al, 2006;Rodgers and DeWeese, 2014;Runyan et al, 2017;Steinmetz et al, 2019).…”

“…However, the underlying integrative circuit mechanisms are still only partially understood. In the case of the auditory system, ample evidence has revealed that the primary auditory cortex (A1) integrates sensory information with other contextual and motor signals and further reflects higher cognitive demands responsible for prediction (Kumar et al, 2011;Parras et al, 2017;Town et al, 2018), choice accuracy (Niwa et al, 2012;Caras & Sanes, 2017), and auditory-guided decision making (Brosch et al, 2005;King et al, 2018;Ohl and Scheich, 2005;Tsunada et al, 2015). The salience of behaviorally relevant sounds further critically depends on the exact reinforcement regimes and task rules (Bagur et al, 2018;David et al, 2012;Huang et al, 2019), which renders the auditory cortex a multifarious integrative circuit.…”

Regulation of perceptual learning by chronic chemogenetic manipulation of parvalbumin-positive interneurons

“…In sensory systems, improved perception can be achieved through practice in specific sensory discrimination tasks, a process known as perceptual learning (PL) (Caras and Sanes, 2017). To test whether PL is associated with changes in PV expression in A1 we trained rats using an adaptive auditory oddball discrimination task (Figure 1A).…”