Adaptation to elastic loads and BMI robot controls during XXXX locomotion examined with point-process GLMs.

@entity35 @entity9420 @entity2109 @entity3628
Currently little is known about how a mechanically coupled BMI system's actions are integrated into ongoing body dynamics. We tested a locomotor task augmented with a BMI system driving a robot mechanically interacting with a @entity35 under three conditions: control locomotion ( @entity2109 ), "simple elastic load" (E) and "BMI with elastic load" (BMI/E). The effect of the BMI was to allow compensation of the elastic load as a function of the neural drive. Neurons recorded here were close to one another in cortex, all within a 200 micron diameter horizontal distance of one another. The interactions of these close assemblies of neurons may differ from those among neurons at longer distances in BMI tasks and thus are important to explore. A point process @entity3628 ( @entity3628 ), was used to examine connectivity at two different binning timescales (1 ms vs. 10 ms). We used @entity3628 models to fit non-Poisson neural dynamics solely using other neurons' prior neural activity as covariates. Models at different timescales were compared based on Kolmogorov-Smirnov (KS) goodness-of-fit and parsimony. About 15% of cells with non-Poisson firing were well fitted with the neuron-to-neuron models alone. More such cells were fitted at the 1 ms binning than 10 ms. Positive connection parameters ("excitation" ~70%) exceeded negative parameters ("inhibition" ~30%). Significant connectivity changes in the @entity3628 determined networks of well-fitted neurons occurred between the conditions. However, a common core of connections comprising at least ~15% of connections persisted between any two of the three conditions. Significantly almost twice as many connections were in common between the two load conditions (~27%), compared to between either @entity9420 and the baseline. This local point process @entity3628 identified neural correlation structure and the changes seen across task conditions in the @entity35 in this neural subset may be intrinsic to cortex or due to feedback and input reorganization in adaptation.
