Sara A. Solla

This study shows that during conditioning tasks, hippocampal activity shifts from spatial to temporally structured patterns, disrupting place coding in rats.

The paper shows that a deterministic drive across brain regions predicts action timing, which synergizes with trial-specific and within-trial noise to time self-initiated actions.

The paper shows that task-related neural population activity maintains a conserved low-dimensional organization across different environments and animals, supporting behavioral generalization despite changes in neural representations.

This paper transfers brain-computer interface decoders from monkeys to humans, showing that motor-related neural representations are consistent across species and reducing the need for extensive data.

The paper investigates whether low-dimensional neural manifolds in M1 are present during unconstrained natural movements and finds they are, with similar dimensionality to constrained movements.

The paper quantifies natural tactile scene statistics using 3D object scans and shows they resemble visual image statistics, with neural models capturing primate somatosensory cortex responses.

This paper evaluates dimensionality estimation algorithms for neural recordings, finds linear methods overestimate nonlinear data, and introduces a deep learning denoising algorithm to improve accuracy.

This study shows that primary sensory neurons in the rodent vibrissal system encode complex 3D tactile stimuli continuously and multidimensionally, without preferring principal components or binary adaptation classes.

The paper introduces a method to characterize learning dynamics in motor control by applying perturbations and analyzing responses to estimate internal dynamics and noise.

The paper shows that hand movements outside a previously identified subspace are structured and task-dependent, indicating volitional control beyond the assumed subspace.

The paper models motor learning as a deterministic dynamical process involving forward and inverse internal models, showing exponential convergence toward a solution.

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

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

The paper validates in silico predictions of inhibitory neuronal connections using a Neural Circuit Probe through experimental interventions.