G. Sean Escola

The paper identifies optimal corticothalamic connectivity structures that support learning in thalamocortical loops for motor control and working memory tasks.

This study shows motor cortex is needed for flexible, cue-driven motor sequences but not for automatic sequences, and this dependency changes when sequences are practiced together.

This study investigates how the sensorimotor striatum contributes to automatic and visually-guided motor sequences in rats, finding that it is essential for automatic skills but not sensory-guided sequences.

This paper introduces a non-parametric method to detect hierarchical confounders in biological datasets and demonstrates its application in biomedical image and multi-phenotypic data.

This paper develops a computational model showing how thalamic control of cortical dynamics enables flexible motor sequencing through motif-specific inhibition.

Demuxalot improves genetic demultiplexing for single-cell RNA sequencing by enhancing accuracy and efficiency in multi-batch, multi-donor studies.

This paper proposes neural and circuit-level mechanisms for motor learning, showing how cortical and subcortical pathways support fast and slow learning processes.

This paper develops an automated system to scale patient-derived brain organoids, revealing consistent donor and batch effects and demonstrating reproducible disease phenotypes in tuberous sclerosis models.

The paper develops a model of the striatum that learns and executes variable-speed sequences through cortical tutoring, addressing challenges in temporal rescaling, sequence flexibility, and biologically plausible learning.