Nicolas Y. Masse

DeepRNAi predicts siRNA on- and off-target activity using deep learning, showing high accuracy across species and genomes, and highlights off-target concerns in potent siRNAs.

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

This paper uses reinforcement learning to discover new mechanisms of reentry in excitable media, including subthreshold stimulation strategies that induce unidirectional propagation and reentry.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

This study creates a single-nucleus transcriptomic atlas of Parkinson's disease progression, identifying cell-type-specific gene expression changes and linking them to genetic risk factors and disease stages.

This paper shows that rapid multitask learning in reservoir-style networks can occur with sparse, localized plasticity and local error signals, without backpropagation.

This study investigates the roles of prefrontal and parietal cortices in sequential categorical decisions using neural recordings and computational models.

The paper shows that perceptual detection in mice depends on integrating spike counts in V1 rather than instantaneous spiking changes.

This study uses RNN models to show that short-term plasticity supports memory maintenance for short delays, while persistent activity emerges for information manipulation tasks.