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1.  CORTICOSTRIATAL PLASTICITY IS NECESSARY FOR LEARNING INTENTIONAL NEUROPROSTHETIC SKILLS 
Nature  2012;483(7389):331-335.
The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills1, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills2–4, it remains unclear if similar circuits or processes are required for abstract skill learning. We utilized a novel behavioral paradigm in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrated that these learned neuroprosthetic actions were intentional and goal-directed, rather than habitual. Striatal neurons changed their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progressed. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerged. Specific deletion of striatal NMDA receptors impaired the development of this corticostriatal plasticity, and disrupted the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.
doi:10.1038/nature10845
PMCID: PMC3477868  PMID: 22388818
2.  A Statistical Description of Neural Ensemble Dynamics 
The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility.
doi:10.3389/fncom.2011.00052
PMCID: PMC3226070  PMID: 22319486
neural ensemble data; spikes; local field potential; data analysis; KL-divergence

Results 1-2 (2)