Nirvik Sinha

This paper develops a deep learning framework to detect kinematic events in primate reaching behavior and assesses its performance across animals.

This study investigates motor deficits and compensatory responses in monkeys with cerebellar outflow blocked during reaching tasks, finding reduced hand velocity and altered movement strategies.

The paper introduces a Bayesian MCMC method with adaptive parallel tempering to estimate parameters and quantify uncertainty in Hodgkin-Huxley neuron models using voltage trace data.