Spectral tuning of adaptation supports coding of sensory context in auditory cortex

Abstract

1 2 Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information 3 over hundreds of milliseconds, but the latency of sound-evoked activity in auditory cortex typically has 4 much shorter latency. It has been observed that the acoustic context, i.e., sound history, can modulate sound 5 evoked activity. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific 6 adaption and contrast gain control. However, an encoding model that links context to natural sound 7processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo 8 adaptation mimicking short-term synaptic plasticity can account for contextual effects during natural sound 9 processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during 10 presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were 11 characterized by a standard linear-nonlinear spectro-temporal receptive field model (LN STRF) and STRF 12 variants that incorporated STP-like adaptation. In two models, STP was applied either globally across all 13 spectral channels or locally to subsets of channels. For most neurons, STRFs incorporating locally tuned 14 STP predicted neural activity as well or better than the LN and global STP STRF. The strength of nonlinear 15 adaptation varied across neurons. Within neurons, adaptation was generally stronger for activation with 16 excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo 17 stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP STRFs 18 were compared between passive and active behavior conditions, response gain often changed, but average 19 STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a 20 mechanism with STP-like dynamics, may support representation of the diverse spectro-temporal patterns 21 that comprise natural sounds. 22Author summary 1 2 Successfully discriminating between behaviorally relevant sounds such as vocalizations and environmental 3 noise requires processing how acoustic information changes over many tens to hundreds of milliseconds. 4The sound-evoked activity measured for most auditory cortical neurons is relatively short (< 50 ms), so it 5 is not clear how the auditory cortex encodes sound information over longer periods. In this study, we 6 propose that nonlinear adaptation, mimicking the effects of short-term synaptic plasticity (STP), enables 7 auditory neurons to encode longer and more complex spectro-temporal patterns. A model in which sound 8 history is stored in the latent state of plastic synapses is able to describe responses of single cortical neurons 9 to natural sounds better than a standard encoding model that does not include nonlinear adaptation. 10 Moreover, STP-like adaptation can account for contextual effects on sound evoked activity that cannot be 11 accounted for by standard e...

Synaptic control of temporal processing in theDrosophilaolfactory system

“…A depressing synapse transmits a large signal when the presynaptic firing rate changes rapidly, but attenuates fluctuations when they are slow (Abbott et al 1997). The computational properties of short-term plasticity have frequently been evoked to explain sensory responses observed within the brain (Espejo et al 2019; Seay et al 2020). However, it has been challenging to directly test the role of synaptic depression in shaping sensory processing in intact organisms.…”

Simple transformations capture auditory input to cortex

“…The datasets used in this study were from extracellular recordings of the responses to sounds of neurons in ferret A1. We used three datasets: responses to natural sounds in anesthetized ferrets [natural sound dataset 1; NS1 (32)], responses to dynamic random chords (DRCs) in the same anesthetized ferrets [DRC dataset (32)], and responses to natural sounds in awake ferrets [natural sound dataset 2; NS2 (33)]. We will focus first on the results with NS1, which consisted of neural responses to a diverse selection of natural sounds (20 sound snippets, each 5 s in duration), including human speech, animal vocalizations, and environmental sounds (17)(18)(19).…”

“…To examine how the choice of stimulus and brain state influences the results, we tested the performance of the models on two other datasets. One of these (NS2) consisted of extracellular recordings from A1 of awake ferrets in response to a different set of natural sounds (18 sound snippets, each 4 s in duration), including human speech, animal vocalizations, music, and environmental sounds (53). In total, 235 single units were included, which had a noise ratio of <40.…”

Dynamics of cortical contrast adaptation predict perception of signals in noise

“…Neurons throughout the auditory system adapt to the statistics of the acoustic environment, including the frequency of stimuli over time 58, 59 , more complex sound patterns 26,60 , and task-related or rewarded stimuli [61][62][63][64][65][66] . Inspired by the latter studies, we intentionally designed our stimuli using unbiased white-noise backgrounds, which allowed us to fit encoding models to our data.…”