Fundamental to understanding how our world is represented in our brain is the ability to observe the collective activity of ensembles of cortical neurons acting in concert, and to correlate this activity with an observed, context-dependent behavior. In the context of closed loop brain machine interfaces, recent studies have demonstrated the ability of cortical neurons to co-modulate their firing patterns in order to control artificial limbs or the subject’s own muscles. Decoding neural activity in this context, however, has been limited by the ability to recognize sufficiently distinct patterns of activity, many of which become highly nonstationary during the acquisition of these novel sensorimotor transformations.
In this talk, I will first describe how the design of a wireless,bi-directional neural interface system is crucial to overcome these limitations to permit instantaneous neural decoding and control in a freely behaving subject to take place. Next, I will show how these features can be used to infer the functional connectivity in local neuronal circuits to permit characterizing and quantifying neural plasticity, for example, after sensory deprivation or during learning new tasks. I will conclude with a brief discussion on the potential of this framework to increase the effectiveness of brain machine/computer interfaces through selective, optimized feedback control techniques to guide and harness neural plasticity that is otherwise brain controlled.
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