Auditory Cortex

Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays1-3 to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus4,5, while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative vocal pitch, onsets, amplitude envelope and sequence statistics. Neurons at each cross-laminar recording exhibited dominant tuning to a primary speech feature while also containing a substantial proportion of neurons that encoded other features contributing to heterogeneous selectivity. Spatially, neurons at similar cortical depths tended to encode similar speech features. Activity across all cortical layers was predictive of high-frequency field potentials (electrocorticography), providing a neuronal origin for macroelectrode recordings from the cortical surface. Together, these results establish single-neuron tuning across the cortical laminae as an important dimension of speech encoding in human superior temporal gyrus.

The human auditory system extracts rich linguistic abstractions from speech signals. Traditional approaches to understanding this complex process have used linear feature-encoding models, with limited success. Artificial neural networks excel in speech recognition tasks and offer promising computational models of speech processing. We used speech representations in state-of-the-art deep neural network (DNN) models to investigate neural coding from the auditory nerve to the speech cortex. Representations in hierarchical layers of the DNN correlated well with the neural activity throughout the ascending auditory system. Unsupervised speech models performed at least as well as other purely supervised or fine-tuned models. Deeper DNN layers were better correlated with the neural activity in the higher-order auditory cortex, with computations aligned with phonemic and syllabic structures in speech. Accordingly, DNN models trained on either English or Mandarin predicted cortical responses in native speakers of each language. These results reveal convergence between DNN model representations and the biological auditory pathway, offering new approaches for modeling neural coding in the auditory cortex.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Auditory steady-state responses (ASSRs) are rhythmic neural oscillations that synchronize to periodic auditory stimulation and serve as a noninvasive index of cortical network dynamics. ASSRs, particularly those at 40 Hz, have received substantial attention as sensitive markers of temporal precision, excitation-inhibition balance, and functional connectivity in the auditory cortex, and have been widely applied in translational research on neurodevelopmental and neuropsychiatric disorders. Because gamma synchronization supports key cognitive functions, including auditory temporal processing, selective attention, speech perception, and early language development, mapping its lifespan trajectory provides insight into how maturing cortical dynamics underpin cognitive development. However, despite extensive clinical use, the normative developmental and aging trajectory of gamma-range ASSRs remains unclear. This systematic review aimed to synthesize evidence on age-related differences in ASSRs measured with EEG or MEG across the human lifespan. Following PRISMA guidelines, searches were conducted in PubMed/Scopus, identifying 40 eligible studies. The findings reveal a pronounced increase in ASSR amplitude and phase-locking from infancy through adolescence, consistent with maturation of inhibitory circuitry, synaptic refinement, and myelination. In adulthood and aging, results were heterogeneous, with studies reporting preserved, diminished, or enhanced 40-Hz synchronization, reflecting diverse methodological approaches and potentially distinct neurobiological changes. Lifespan coverage across studies was uneven, with sparse data in early childhood and older adulthood, and limited longitudinal evidence. The review suggests a nonlinear trajectory characterized by developmental strengthening, adult stability, and variable age-related change. Comprehensive lifespan-spanning and longitudinal studies are needed to establish normative patterns and improve the interpretability of ASSR alterations in clinical populations.