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Motor control refers to the process by which the nervous system coordinates the muscle and limbs to achieve a desired movement or set of actions. This includes the ability to anticipate, adjust and respond to deviations from the desired action.
Using recurrent neural networks, here the authors show that learning the same task through different experiences can lead to important differences in how neural activity is structured. These differences can play a crucial role for subsequent adaptation, with networks that are equally good at the initial task showing opposing trends in adaptation.
Wandelt et al. describe a brain–machine interface that captures intracortical neural activity during internal speech (words said within the mind with no associated movement or audio output) and translates those cortical signals into real-time text.
Prosthetic embodiment, or the incorporation of a prosthesis into one’s sensory and functional body schema, may be achieved by engineering bionic limbs that leverage a closed-loop mechanoneural–machine interface. However, the subjective experience of embodiment remains difficult to define and assess.
The main direction of motor skill-specific information between rat primary motor cortex and dorsolateral striatum is shown to switch from cortex-predominant before learning to striatum-predominant after learning.