Topic: Motor output

A one-year machine-learning research internship with the Allen Institute Neural Dynamics accelerator. The intern will analyze recordings from two genetically distinct neural populations in the striatum alongside motor output, muscle activity and behavioral video. The project asks how shared and population-specific neural activity encodes movement, using latent-variable models, regression, classification and cross-validation. Work includes reproducible Python analysis and communicating results through figures and methods that may contribute to a poster or publication. No previous neuroscience b

Motor adaptation is a widely-used paradigm for understanding short-term learning. However, it is unknown how existing skillsets acquired through the long-term learning process that begins in utero can affect motor adaptation. Long-term learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr

The mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhib