What determines how we move in the world? Motor neuroscience often focusses either on intrinsic rhythmical properties of motor circuits or extrinsic sensorimotor feedback loops. Here we show that the interplay of both intrinsic and extrinsic dynamics is required to explain the intermittency observed in continuous tracking movements. Using spatiotemporal perturbations in humans, we demonstrate that apparently discrete submovements made 2-3 times per second reflect constructive interference between motor errors and continuous feedback corrections that are filtered by intrinsic circuitry in the motor system. Local field potentials in monkey motor cortex revealed characteristic signatures of a Kalman filter, giving rise to both low-frequency cortical cycles during movement, and delta oscillations during sleep. We interpret these results within the framework of optimal feedback control, and suggest that the intrinsic rhythmicity of motor cortical networks reflects an internal model of external dynamics, which is used for state estimation during feedback-guided movement.
Dataset 1 - Human tracking with feedback delays
This dataset contains behavioural data from 8 human subjects performing isometric visuomotor tracking with feedback delays between 0-500 ms.
Experiment1.mat
Dataset 2 - Human tracking with perturbations and feedback delays
This dataset contains behavioural data from 8 human subjects performing an isometric visuomotor tracking experiment with 0-5 Hz perturbations and 0 or 200ms feedback delays.
Experiment2.mat
Dataset 3 - Monkey tracking with feedback delays
This dataset contains behavioural and electrophysiological data from a monkey performing visuomotor tracking with feedback delays of 0-600ms.
Experiment3.mat
Dataset 4 - Monkey tracking with feedback delays
This dataset contains behavioural and electrophysiological data from a second monkey performing visuomotor tracking with feedback delays of 0-600ms.
Experiment4.mat
Analysis code for Dataset 1
Sample analysis code to calculate power spectra of cursor angular velocity for all subjects in Dataset 1.
analyse1.m
Analysis code for Dataset 2
Sample analysis code to calculate cursor and force power spectra, amplitude responses and phase delays for all subjects in Dataset 2.
analyse2.m
Analysis code for Dataset 3
Sample analysis code to calculate cursor and local field potential power spectra, cursor-LFP coherence and LFP-LFP imaginary coherence spectra for all sessions in Dataset 3.
analyse3.m
Analysis code for Dataset 4
Sample analysis code to calculate cursor and local field potential power spectra, cursor-LFP coherence and LFP-LFP imaginary coherence spectra for all sessions in Dataset 4
analyse4.m
Optimal Feedback Control model
Code to calculate transfer functions for an optimal feedback controller incorporating state estimation using delayed feedback. The code plots cursor and force amplitude responses and phase delay.
ofc_model.m