Comparing Offline Decoding Performance in Physiologically Defined Neuronal Classes

Objective: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths

Outperforming the S&P 500 Using Top-Down Asset Allocation

This paper investigates whether portfolio managers can outperform the S&P 500 index using top-down asset allocation, using historical returns, standard deviations, and correlations of different asset classes. Efficient diversification between asset classes reduces the idiosyncratic risk by selecting assets from a wide variety of different classes of assets in different parts of the world. The goal is to create an optimization model that makes it possible for a portfolio manager to generate higher expected returns, while taking risk equal to that of the S&P 500, or incur lower risk while generating the same expected return of the S&P 500