Ian H. Stevenson

The paper predicts speech recognition accuracy across diverse voices, words, and background noises using brain-inspired auditory features.

This study decodes lever-pressing behavior from multi-region LFP in rats across sex, task, and dopamine depletion conditions, showing stable decoding across animals and conditions.

The paper investigates how background noise statistics affect speech encoding in the auditory midbrain of rabbits, finding that neural response variability depends on background spectrum and modulation statistics.

The paper tracks long-term changes in hippocampal excitatory-to-inhibitory synapses using spike data from mice, finding that covariance of pre- and postsynaptic activity improves prediction of synaptic efficacy fluctuations.

The paper develops parametric models to predict nonstationary spike-spike correlations using LFP features and applies them to track changes in correlations over time in mouse hippocampus and visual cortex data.

The paper compares how CA1 cells in dorsal and intermediate hippocampal regions process spatial and social/odor novelty, finding similar responses despite differences in spatial tuning.

This paper identifies overconfidence in Bayesian decoders of neural spiking activity and proposes methods to correct miscalibration in various experimental settings.

The paper shows human speech recognition in natural noise relies on mid-level auditory statistics, with a model predicting perceptual variance better than cochlear models.

The paper investigates how bandwidth scaling in auditory filters improves the neural coding of natural sounds by whitening representations across frequency, time, and modulation dimensions.

The paper examines how natural variations in presynaptic firing rates affect excitatory synaptic strength over slow timescales in vivo.

The paper shows that inhibitory neurons dominate the rich-club structure in cortical microcircuits, influencing network dynamics through increased synchrony and longer neuronal avalanches.

The paper shows that distinct neural response statistics in the auditory system encode sound texture features for recognition and discrimination, with fast spectrum-based discrimination and slow correlation-based recognition.

This paper develops a statistical model to detect synaptic connections from spike recordings by separating fast synaptic effects from slow background fluctuations and incorporating network properties.

The paper develops a statistical model to disentangle synaptic and non-synaptic effects on spike transmission using pre- and postsynaptic spiking data from in vivo recordings.

The paper shows that correlation statistics between frequency-organized auditory channels can be used to identify sound categories with high accuracy, mirroring human performance.

The paper shows that modeling stimulus-dependent variability improves neural decoding accuracy by using alternative models like CMP and NB instead of the Poisson model.

This paper shows how omitted variables in GLMs can bias parameter estimates in neural spiking activity models, using case studies from motor cortex, hippocampus, and visual cortex.

This paper develops two models to estimate short-term synaptic plasticity from pre- and postsynaptic spike data, showing they can accurately reconstruct synaptic dynamics.