Our brain can orchestrates highly complex muscles activations to create the whole wealth of movements that are essential for most of our daily activities. But what if the connection between the brain and the motor plant is severed, like in the case of spinal injuries, or when the motor periphery itself is damaged? Recent advances in brain research suggest that externally monitored brain signals may be re-routed through appropriate electronic interfaces to control either artificial motor prostheses or to natural residual motor functions. This project aims at probing this exciting possibility to develop motor neural prostheses.
Our specific objectives are:

1. to develop adaptive models/mechanisms
that can extract intended movements in real-time on the basis of simultaneously recorded multiple neuronal activities in motor areas of the cortex,

2. to use these models to investigate how neuronal representations of purposeful arm movements are generated and modified during sensorimotor learning, and

3. to use these models as the core of an adaptive mechanism that transforms neuronal activity in real-time to desired movements, in a way that best represents the subject's intention, while continuously compensating for possible changes of the sampled neural activity.

We pursue these objectives in a coordinated effort of experimental and theoretical approaches, combining psychophysical experiments on human subjects, neurophysiological recordings in behaving monkeys, advanced data analysis and modeling work.

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