Stephen V. David

The paper develops ACNet, a foundation model of auditory cortex sound encoding, showing it can predict neural activity across animals and reveal generalizable coding principles.

The paper develops a linear-nonlinear subspace model that captures the encoding properties of convolutional neural networks in auditory cortex, showing they functionally match CNNs and reveal cell-type and layer-specific encoding differences.

The paper investigates how auditory cortex encodes long-lasting contextual effects in natural sounds using neural population activity.

The paper shows that in auditory cortex, foreground sounds are suppressed when presented with background sounds, and this suppression is linked to spectro-temporal statistics of the sounds.

The paper shows that task engagement leads to categorical sound representations in non-primary auditory cortex, while primary auditory cortex shows general sound decoding enhancement.

The paper develops a population encoding model using CNNs to predict auditory neuron activity across natural sounds, showing improved generalizability compared to traditional models.

This study investigates how acute vagus nerve stimulation (VNS) affects auditory learning and neural activity in ferrets over 1-2 days, finding increased learning rates and specific neural response reductions in primary auditory cortex.

This paper introduces a simple and interpretable dimensionality reduction method for neural population decoding, demonstrating its reliability with limited experimental trials.

The paper shows that call feature selectivity emerges in superficial layers of primary auditory cortex through changes in neural representation.

The paper isolates task engagement and arousal effects in auditory cortex and midbrain using single-unit recordings and generalized linear modeling, finding distinct neural population responses to each.

The paper compares two models of auditory coding, STP and GC, and finds that a hybrid model combining both mechanisms better captures neural responses to natural sounds.

The paper investigates how arousal affects neural coding of natural sounds in auditory cortex by analyzing changes in response magnitude and noise correlations.

This paper develops an interleaved stimulus design and open-source software to optimize auditory brainstem response acquisition by reducing adaptation effects and acquisition time.

The paper demonstrates stream segregation in auditory cortex by showing enhanced neural responses to repeating foreground stimuli compared to background noise in ferrets.

The paper examines how pupil size fluctuations affect neural activity and selectivity in auditory cortex, finding increased excitability but stable stimulus selectivity.

The paper develops a model of spectral adaptation in auditory cortex that incorporates short-term synaptic plasticity to better predict neural responses to natural sounds than standard models.

The paper shows that primary auditory cortex encodes stimulus meaning more during task engagement than passive listening, with population-level changes in encoding.