Lopez Espejo, M. | Search bioRxiv

bioRxiv2025

Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate

Much remains unknown about the computations that allow animals to flexibly integrate across multiple timescales in natural sounds. One key question is whether multiscale integration is accomplished by diverse populations of neurons, each of which integrates information within a constrained temporal window, or whether individual units effectively integrate across many different temporal scales depending on the information rate. Here, we show that responses from neurons throughout the ferret auditory cortex are nearly completely unaffected by sounds falling beyond a time-limited "integration window". This window varies substantially across cells within the auditory cortex ([~]15 to [~]150 ms), increasing substantially from primary to non-primary auditory cortex across all cortical layers, but is unaffected by the information rate of sound. These results indicate that multiscale computation is predominantly accomplished by diverse and hierarchically organized neural populations, each of which integrates information within a highly constrained temporal window.

neuroscience↗

bioRxiv2023

Sensory context for coding of natural sounds in auditory cortex

Accurate sound perception can require integrating information over hundreds of milliseconds or even seconds. Spectro-temporal models of sound coding by single neurons in auditory cortex indicate that the majority of sound-evoked activity can be attributed to stimuli with a few tens of milliseconds. It remains uncertain how the auditory system integrates information about sensory context on a longer timescale. Here we characterized long-lasting contextual effects in auditory cortex (AC) using a diverse set of natural sound stimuli. We measured context effects as the difference in a neurons response to a single probe sound following two different context sounds. Many AC neurons showed context effects lasting longer than the temporal window of a traditional spectro-temporal receptive field. The duration and magnitude of context effects varied substantially across neurons and stimuli. This diversity of context effects formed a sparse code across the neural population that encoded a wider range of contexts than any constituent neuron. Encoding model analysis indicates that context effects can be explained by activity in the local neural population, suggesting that recurrent local circuits support a long-lasting representation of sensory context in auditory cortex.

neuroscience↗

bioRxiv2019

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

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

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