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Brain-Machine Interface Paradigms for Neuroscience and Clinical Translation - Samantha R. Santacruz, Vivek R. Athalye, Ryan M. Neely, and Jose M. Carmena
Pages 57-60

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From page 57...
... A closed control loop is established by providing the subject with visual and sensory feedback of the prosthetic device. BMIs also provide a framework for examining basic neuroscience questions, especially those related to the understanding of how neural plasticity relates to the acquisition and consolidation of skills.
From page 58...
... Next we discuss how to use BMIs to study skill learning and consolidation. In addition to holding great therapeutic potential as assistive and rehabilitation technology, BMIs provide a powerful framework for examining basic neuroscience questions, especially those related to the neural correlates of learning behavior, as it offers researchers the unique opportunity to directly control the causal relationship between neuronal activity and behavioral output.
From page 59...
... Hence, our findings describe neuroprosthetic skill learning as a process of spatiotemporal neural pattern consolidation, whereby the strengthening of task-relevant input signals coordinates initially variable, high-dimensional activity. A greater understanding of the neural substrates of neuroprosthetic skill learning can provide insight into the mechanisms of natural sensorimotor learning as well as help guide the design and development of neurobiologically informed neuroprosthetic systems to aid people with devastating neurological conditions.
From page 60...
... In combination with new physiological biomarkers and animal models, future BMI neurotherapeutic devices will have the potential to cure people suffering from psychiatric and mood disorders. Toward this goal, we have developed a novel animal model for assaying correlates of acute anxiety and closed-loop strategies for mood modulation with strong anxiolytic effects.


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