Maxime Beau

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

This paper develops a deep-learning classifier to identify cell types from extracellular recordings across species with over 95% accuracy.

Multi-region coordination of evidence accumulation and decision commitment in the brain

This study investigates decision-making phases in the brain using simultaneous recordings, supporting a single accumulator model and revealing abrupt covert commitment transitions.

Domain-adaptive matching bridges synthetic and in vivo neural dynamics for neural circuit connectivity inference

This paper introduces DeepDAM, a framework that improves neural connectivity inference by aligning synthetic and in vivo data domains, achieving high accuracy in rodent and other systems.

Alterations of the axon initial segment in multiple sclerosis

The paper investigates axon initial segment alterations in multiple sclerosis, finding increased soma-axon initial segment gap in both active and inactive lesions.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

The paper introduces Neuropixels 2.0, a high-density probe for long-term brain recordings, and demonstrates its stability across months in multiple labs and species.

A rapid and efficient learning rule for biological neural circuits

This paper introduces DGNs, a biologically plausible learning rule that outperforms backpropagation in efficiency and forgetting resistance, inspired by cerebellar structure and function.