A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
A Spike-Train Probability Model
Neural Computation
Automatic spike sorting using tuning information
Neural Computation
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used with any of the current decoding algorithms, such as population vector or likelihood-based algorithms. Based on analytical results and an extensive simulation study, we show that our paradigm is comparable to, and sometimes more efficient than, the traditional approach based on well-isolated neurons and that it remains efficient even when all electrodes are severely corrupted by noise, a situation that would render spike sorting particularly difficult. Our paradigm will also save time and computational effort, both of which are crucially important for successful operation of real-time brain-machine interfaces. Indeed, in place of the lengthy spike-sorting task of the traditional approach, it involves an exact expectation EM algorithm that is fast enough that it could also be left to run during decoding to capture potential slow changes in the states of the neurons.