Given a wavelet w and a noisy trace t + s * w + n, an approximation ŝ of the spike train s can be obtained using the [Formula: see text] norm. This extraction has the advantage of preserving isolated spikes in s. On some types of data the spike train ŝ can represent s as a sparse series of spikes, which may be sampled at a rate higher than the sample rate of the data trace t. The extracted spike train ŝ may be qualitatively much different than those commonly extracted using the [Formula: see text] norm. The [Formula: see text] norm can also be used to extract a wavelet ŵ from a trace t when a spike train s is known. This wavelet extraction can be constrained to give a smooth wavelet which integrates to zero and goes to zero at the ends. Given a trace t and an initial approximation for either s or w, it is possible to alternately extract spike trains and wavelets to improve the representation of trace t. Although special algorithms have been developed to solve [Formula: see text] problems, all of the calculations can be performed using a general linear programming system. Proper weighting procedures allow these methods to be used on ungained data.
Development of a Menu-driven User Interface System FSME for Mathemantical Programming System "micro-NAPS": Focusing on Stochastic Linear Programming