Neural population partitioning and a concurrent brain-machine interface for sequential motor function.

1] Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. [2] Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Harvard Medical School, Boston, Massachusetts, USA.
Nature Neuroscience (Impact Factor: 14.98). 11/2012; DOI: 10.1038/nn.3250
Source: PubMed

ABSTRACT Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.

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    ABSTRACT: Neuroscientific studies of execution of the drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about mathematical ideas behind splitting and merging geometric and temporal features which characterize biological movements. Movement primitives supposedly facilitate the efficiency of movements' representation in the brain and comply with different criteria for biological movements, among them kinematic smoothness and geometric constraint. Criterion for trajectories' "maximal smoothness" of arbitrary order $n$ is employed, $n = 3$ is the case of the minimum-jerk model. I derive a class of differential equations obeyed by movement paths for which $n$th order maximally smooth trajectories have constant speed. The speed is invariant under a class of geometric transformations. Equations' solutions presumably serve as candidates for geometric movement primitives. The speed here is defined as the rate of accumulating geometric measurement along the drawn path. The geometric measurement may be chosen to be an arc in certain geometry. For example the two-thirds power-law model corresponds to piece-wise constant speed of accumulating equi-affine arc. The derived class of differential equations consists of two parts. The first part is identical for all geometric parameterizations of the path. The second part is parametrization specific and is needed to identify whether a solution of the first part indeed represents a curve. Corresponding counter-examples are provided. Equations in different geometries in plane and in space and their known solutions are presented. The derived class of differential equation is a novel tool for discovering candidates for geometric movement primitives.

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