Kristofer E. Bouchard

The paper uses optogenetics to determine the laminar origins of high-gamma and low-frequency ECoG signals in mice, finding that L5 suppression affects high frequencies while L2/3 suppression affects low frequencies.

This study uses statistical tests to infer partially observable Markov models of syllable transitions in Bengalese finch songs, showing that auditory feedback is crucial for context-dependent transitions.

The paper trains an autoregressive Transformer on neural spiking data to decode motor behavior, showing broad utility across tasks but highlighting limitations in scaling.

The paper identifies and mitigates bias in neural data models by resolving non-identifiability, showing that coupling is overestimated and tuning is underestimated in common methods.

BigNeuron benchmarks automatic neuron reconstruction algorithms using gold standard annotations and image quality metrics, showing that consensus methods outperform individual algorithms.

The paper analyzes cortical surface electrical potentials (CSEPs) to show that high-gamma components are band-limited, not broadband, and identifies multiple band-limited frequency components in ECoG signals.

The paper describes the Neurodata Without Borders (NWB) ecosystem, which enables FAIR neurophysiological data management and analysis across species and modalities.

This study investigates the localization and origins of sensory-evoked ECoG signals, finding they originate primarily from infragranular layers of a single cortical column in rats.

The paper shows that correlated variability in neural populations leads to highly suboptimal sensory coding according to novel null models.

NeuroGPU accelerates biophysically detailed neuronal simulations on GPUs, enabling faster parameter exploration and model fitting.

The paper applies an improved statistical inference framework to neuroscience models, finding more accurate and sparse parameter estimates that reshape interpretations across multiple datasets.

This paper introduces a compact neural network with mixture z-scoring to automate sleep scoring, showing it avoids systematic bias present in standard methods.

This paper uses CNNs to infer neuronal ionic conductances from simulated membrane potentials, achieving better accuracy than traditional optimization methods.

Version 2.0 of the Neurodata Without Borders neurophysiology format centres on the schema, with an accompanying software stack and community aimed at making recordings easier to reuse.

The paper validates in silico predictions of inhibitory neuronal connections using a Neural Circuit Probe through experimental interventions.