Jesse I. Gilmer

This paper uses a computational model to study how coordinated reset stimulation can reduce neuronal synchrony in Parkinson's disease by adjusting synaptic weights through spike-timing-dependent plasticity.

This paper develops a biomechanical model of the mouse distal forelimb to simulate fine motor control and validate it against experimental data.

The paper develops a biomechanical model of the mouse forelimb to predict muscle activity during reaching movements, finding strong similarity between simulated and empirical muscle patterns.

This study trains a minimalist cerebellar cortex model to learn time-series data, revealing that dense granule cell activity and task-specific pattern separation features underlie temporal learning.

The paper identifies monosynaptic inputs to excitatory and inhibitory neurons in the intermediate and deep layers of the superior colliculus, revealing differences in input strength and distribution.

The paper investigates how Golgi cells integrate mossy fiber inputs to regulate granule cell activity in mice, revealing the dominance of slow spillover inhibition.

This study models cerebellar granule cell combinatorial diversity, showing that anatomical constraints lead to input redundancy and support both combinatorial and temporal expansion.