Jeremy F. Magland

Alex H. Barnett, Jeremy F. Magland, Leslie F. Greengard

We describe a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given electrophysiological recording, when ground-truth is unavailable. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the noise model, nor about the internal workings of the sorting algorithm. Such stability is a prerequisite for reproducibility of results. We illustrate the metrics on standard sorting algorithms for both in vivo and ex vivo recordings. We believe that such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms.

Jeremy F. Magland

We introduce a discrete analogue of the scattering theory for the Zakharov-Shabat (ZS) system, and use it to continue the work of C.L. Epstein by deriving an efficient, recursive algorithm for generating RF-pulses for nuclear magnetic resonance (NMR). In the process, we present a straightforward derivation of the standard Gel'fand-Levitan-Marchenko (Marchenko) equations, and we derive similar equations which apply to the discrete framework. In addition, we prove that the potentials obtained by solving the Marchenko equations produce the correct scattering data. We explain how the generally accepted Shinnar-Le Roux (SLR) technique fits into the more general framework of discrete inverse scattering, and we show, using examples, how inverse scattering theory can be used to produce pulses which are, in some ways, superior to standard SLR pulses.