Olivier Winter

The paper introduces a new quality metric for electrophysiological recordings that adapts to varying refractory period durations across brain regions and species.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

This study evaluates the reproducibility of electrophysiological measurements across ten labs using standardized protocols and finds that variability in electrode targeting and statistical methods affects reproducibility.

UnitRefine automates spike sorting curation using machine learning models trained on expert annotations, achieving human-level performance across species and datasets.

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

The paper introduces two public websites for exploring large-scale electrophysiology data from the International Brain Laboratory, enabling cross-platform access to behavioral trials and neuronal data.

This paper introduces a spike sorting-free decoding method using density-based modeling to improve neural decoding performance without explicitly clustering spikes.

DARTsort improves spike sorting by incorporating drift tracking, outperforming Kilosort 2.5 on simulated data with drift and noise.

This paper evaluates compression strategies for electrophysiology data, finding that lossy compression can significantly reduce file sizes without affecting spike sorting accuracy.