Juan Álvaro Gallego

The paper shows that neural dynamics during movement are conserved across mice, monkeys, and humans, despite phylogenetic differences.

This paper proposes a computational framework where the cortico-cerebellar network learns low-dimensional action embeddings and the basal ganglia use reinforcement learning to learn value and policies, demonstrating its relevance to motor learning.

This study shows that neural signatures of motor memories emerge naturally during learning in a BCI paradigm, reflecting task savings without requiring specific mechanisms.

This study shows that feedback-based motor control and motor adaptation share a common implementation in M1 circuits, as demonstrated through a recurrent neural network trained with error feedback.

The paper develops a task and models to show that motor cortex and striatum jointly specify movement parameters during a reach-to-pull action in mice.

The paper shows that neural population dynamics are preserved across individuals of the same species performing similar behaviours, even when the brain structures differ.

Across monkey, mouse and human motor cortex and mouse striatum, population activity is reported to occupy curved rather than flat low-dimensional spaces, most clearly in complex tasks.

The paper shows that neural activity structure from prior learning influences adaptation, with more structured networks adapting better under specific conditions.

The paper uses a network model to show that small, correlated changes in synaptic connectivity can explain motor learning without disrupting neural covariance, aligning with experimental observations.

The paper examines the relationship between local field potentials (LFPs) and latent dynamics in primate sensorimotor cortex, finding frequency-dependent and region-specific correlations.

This paper uses a deep learning model to estimate muscle activation from EMG signals, showing improved performance over traditional methods in predicting joint kinematics and force.

The paper shows that latent neural dynamics in the cortex remain stable over two years, enabling consistent behavior decoding despite neuronal turnover.

The paper demonstrates a neural population mechanism in motor cortices that enables rapid behavioral adaptation without altering functional connectivity.

The paper investigates how motor cortical populations generate varied behaviors by analyzing neural modes across different motor tasks.