Sebastian A. Bruijns

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

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 applies a hybrid model to mouse decision-making data to uncover cognitive processes by combining theory-driven and data-driven approaches.

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.

The paper introduces an infinite hidden semi-Markov model to analyze learning curves in mice, finding three major learning stages and behavior changes after rest periods.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.