Behavior and cognition emerge from the coordinated activity of populations of interacting neurons. To change behavior we must change the architecture of the network that drives behavior. What are the rules whereby the activity of populations of neurons can change during learning? If we could observe the neural population dynamics that underlie skill learning, we might be able to facilitate learning. This ability may eventually lead to improvements to neurally- based rehabilitation strategies that can facilitate the recovery from stroke. We will use a neurofeedback paradigm to shape the neural population dynamics that underlie skill learning. To do this, we will record the activity of dozens of neurons in the motor cortex of Rhesus monkey subjects. Animals will control a cursor on a computer screen by generating neural command signals. This is neurofeedback because the animal directly observes a projection of his neural activity, in the form of the movement of the onscreen cursor. This paradigm allows us to study learning simply by perturbing the mapping from neural activity to cursor movement on the screen. Following such a perturbation the animal must discover how to generate new patterns of neural activity that are now appropriate to restore good control of the onscreen cursor. We will ask two linked questions. First, how does the animal learn to generate new patterns of neural activity? And second, can we facilitate that process? Neurofeedback-based learning offers complementary advantages to arm movements for studying the neural population dynamics that accompany learning. Chie?y, only in a neurofeedback paradigm can we be certain that the learning-induced changes in neural activity we observe matter directly for behavior, because only the neurons we record directly impact behavior in this paradigm. We will test classic theories of skill learning, converted into speci?c hypotheses about how neural activity patterns will change throughout the multi-day course of skill learning. If our neurofeedback-based incremental training schemes do facilitate learning, then this research will inform the work of rehabilitation specialists who work with stroke patients. We (and others) believe that if neurofeedback-based therapies are coupled with standard behavioral therapies for stroke, rehabilitation outcomes will improve.
Stroke can be a debilitating condition. Only ?fty percent of stroke survivors make a full recovery, and that is usually only after extensive rehabilitation. The recovery from stroke and skill learning are probably closely related. We propose to develop brain-computer interface neurofeedback paradigms to facilitate the learning of new skills. If this is effective in our animal subjects it will provide new avenues to explore for stroke rehabilitation.
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