Normal brain function requires constant adaptation, as an organism interacts with the environment and learns to associate important sensory stimuli with appropriate motor actions. Neurological disorders may disrupt these learned associations, and require the nervous system to reorganize itself. As a consequence, neural plasticity is both a crucial component of normal brain function and a critical mechanism for recovery from injury. Here, we use a rat model to develop the computational and experimental tools necessary to describe changes in the way small networks of sensorimotor neurons interact and process information. We develop a statistical model, called the inferred functional connectivity (IFC) model, that can describe quantitatively the influences that observed neurons have on each other in vivo. We use this model to show how some of the correlations seen among neurons have a profound impact on the statistics of multielectrode records, and what sorts of firing patterns are observed and not observed. We show that the structure is low-dimensional and nonlinear, or curved. We then use the IFC algorithm to facilitate the study of plasticity in the awake, behaving animal. We show that by repetitively pairing the recorded spikes of one neuron with electrical stimulation of another, we can strengthen the inferred functional connection between the two neurons. We then show that repetitively pairing electrical stimulation at two sites can produce qualitatively similar changes in functional connectivity. We use a similar stimulation protocol to enhance the ability of a trained rat to detect intracortical microstimulation (ICMS). These results provide an important proof of concept, demonstrating the feasibility of using Hebbian conditioning protocols to change information flow in the brain. Techniques like this may offer patients with neurologic injury improved function through improved neurorehabilitation and bidirectional brain-machine interfaces.
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