David Carlson

A multisite LFP network (EN-Withdrawal), found with supervised dCSFA-NMF in fentanyl-withdrawing mice, classifies morphine withdrawal, tracks behavioral severity, and its rise predicts fentanyl self-administration escalation in experienced mice.

This study investigates how sleep spindles interact with epileptic activity in thalamocortical circuits, finding bidirectional relationships and suggesting spindles stabilize network stability.

This study shows that sleep fragmentation increases local epileptic activity in specific brain regions without affecting broader network propagation.

This study identifies diverse TCR repertoires in CD4+ MAIT cells, showing they use non-TRAV1-2 TCRs and may recognize alternative MR1 ligands.

The paper examines how brain regions involved in emotion regulation encode sleep-wake states in mice using electrophysiological recordings and develops classifiers to distinguish sleep states from neural activity.

This study uses subcutaneous EEG to track sleep patterns over a year, finding that external factors like season, temperature, and weekends influence sleep duration and timing.

The paper identifies CD8+ NK cells as a Mtb-responsive population with enhanced maturity and cytotoxicity in human TB infection, correlating with disease resistance.

The paper identifies a multi-region brain network that encodes anxiety in healthy and depressive states using electrical recordings and machine learning in mice.

The paper shows that editing a specific brain circuit in mice reduces violent behavior without affecting non-aggressive social interactions.

This study shows that prenatal exposure to environmental toxins and stress impairs microglial function and leads to behavioral issues in male mice.

The paper identifies a brain network encoding socioemotional behavior using machine learning on electrical activity from mice during social interactions.

YASS sorts spikes from multi-electrode arrays with > 500 electrodes using neural network detection and denoising, parallelized nonparametric Bayesian clustering, and matching-pursuit deconvolution, outperforming manual sorting on primate retina recordings.

The paper identifies a spatiotemporal dynamic network in the brain associated with depression vulnerability using machine learning and LFP data from stressed mice.

A dense multi-electrode array spike sorter that triages collisions, clusters clean waveforms with a coreset Dirichlet process mixture, then deconvolves, giving more stable clusters and faster template learning than KiloSort.