Hari Teja Kalidindi

This study examines how the central nervous system uses different feedback control strategies during reaching tasks in varying environments, finding that robust control and online adaptation are used differently depending on predictability.

This paper introduces a linear model that explains how neural population dynamics in the motor cortex can flexibly control multiple behaviors through optimal sensory feedback mapping.

This paper shows that movement duration in reaching tasks can be explained by infinite horizon optimal feedback control, capturing tradeoffs between duration, accuracy, and task parameters.

This study shows that sequence production can be either separate or coarticulated based on task-dependent control, validated through simulations and human experiments.

The paper demonstrates that increased spinal reflex responses to loads reduce motor cortex activity, using a neural network model to show how lower-level feedback influences goal-directed motor corrections.

This paper develops a spiking neural network model of the cerebellum with dual STDP processes to explain how saccadic eye movements are controlled for accuracy and vigor.

The paper shows that rotational dynamics in motor cortex can arise from sensory feedback, even without recurrent connections, and are also present in somatosensory cortex and feedback signals.