Leslie S. Smith

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Spike detection and spike sorting techniques are often difficult to assess because of the lack of ground truth data (i.e., spike timings for each neuron). This is particularly important for in vitro recordings where the signal to noise ratio is poor (as is the case for multi-electrode arrays at the bottom of a cell culture dish). We present an analysis of(More)
We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN(More)
We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola et al. 1990) can support an error-correcting learning rule. The rule increases weights when both preand postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold(More)
A biologically inspired technique for detecting onsets in sound is presented. Outputs from a cochlea-like filter are spike coded, in a way similar to the auditory nerve (AN). These AN-like spikes are presented to a leaky integrate-and-fire neuron through a depressing synapse. Onsets are detected with essentially zero latency relative to these AN spikes.(More)
The treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach—GESI. We compare our algorithms with 25 popular missing data imputation algorithms(More)