Prof. Dr. David Franklin

Primary research focus: Behavioral & Cognitive Neuroscience

Keywords: Motor control, motor learning, impedance control, computational neuroscience, human, sensorimotor integration, robotics and virtual reality, modelling, controlled behavioural tasks

Research methods: Computational Modelling, Robotics, Virtual reality, Human Behavior

Brief research description: We investigate the physiological and computational principles of human neuromuscular motor control. Our research examines how the nervous system controls the mechanical properties of the body to adapt to our external environment and produce skilful movement. To examine the computations underlying sensorimotor control, we blend computational and experimental approaches including robotics and virtual reality.

Selected publications:

Česonis J and Franklin DW (2020) Time-to-target explains task-dependent modulation of temporal feedback gain evolution. eNeuro 7(2): ENEURO.0514-19.2020

Yeo S-H, Franklin DW and Wolpert DM (2016) When optimal feedback control is not enough: feedforward strategies are required for optimal control with active sensing. PLoS Comp Biol 12(12): e1005190. doi:10.1371/journal. pcbi.1005190

Sheahan HR, Franklin DW and Wolpert DM (2016) Motor planning, not execution, separates motor memories. Neuron 92, 773-779

Franklin DW, Reichenbach A, Franklin S and Diedrichsen J (2016) Temporal evolution of spatial computations for visuomotor control. Journal of Neuroscience 36, 2329-2341

Howard IS, Wolpert DM and Franklin DW (2015) The value of the follow-through derives from motor learning depending on future actions. Current Biology 25: 397-401

Franklin DW and Wolpert DM (2011) Computational mechanisms of sensorimotor control. Neuron, 72: 425-442