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A novel family of non-parametric cumulative based divergences
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Research Article Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
"... Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and pa ..."
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Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98 % reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04 % reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. 1.
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"... 1Kernel methods on spike train space for neuroscience: a tutorial ..."
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, 2010
"... It would be only appropriate to thank my advisor Dr. Jose ́ Carlos Santos Carvalho Pŕıncipe first for his guidance and lessons not only for research but for life in general. A lot of people helped me get through my journey of graduate school, and perhaps my attempt to properly thank them all will f ..."
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It would be only appropriate to thank my advisor Dr. Jose ́ Carlos Santos Carvalho Pŕıncipe first for his guidance and lessons not only for research but for life in general. A lot of people helped me get through my journey of graduate school, and perhaps my attempt to properly thank them all will fail miserably, but I have to try. Dr. Thomas B. DeMarse helped me enormously especially by letting me perform experiments, and he has been emotionally supporting my research as well. I owe my deepest gratitude to Dr. Murali Rao for bringing mathematical rigor to my clumsy ideas. My committee members Dr. Arunava Banerjee, Dr. Bruce Wheeler and Dr. Justin Sanchez supported me and kept me motivated. Dr. John Harris’s kind support allowed me to make friends and connections around the world. Dr. Purvis Bedenbaugh brought me a special Christmas gift of auditory spiking data in 2009. I am indebted to many of my colleagues; without their support this dissertation would not have been possible. António Rafael da Costa Paiva has been a great friend and colleague for developing spike train based signal processing algorithms. Jianwu Xu and Weifeng Liu gave me great intuitions for reproducing kernel Hilbert spaces. Dongming Xu enlightened me on dynamical systems. Brain storming with Karl Dockendorf was always a pleasure. I learned so much from the discussions with Steven Van Vaerenbergh and Luis Sanchez. Among all the most fruitful collaboration was with Sohan Seth. He has been a great friend, and brought joy to my work. I greatly appreciate all the support my friends gave me in a number of ways. I only mention a few of them here: Pingping Zhu the operator operator, Jason Winters
A METRIC APPROACH TOWARD POINT PROCESS DIVERGENCE
"... Estimating divergence between two point processes, i.e. probability laws on the space of spike trains, is an essential tool in many computational neuroscience applications, such as change detection and neural coding. However, the prob-lem of estimating divergence, although well studied in the Euclid ..."
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Estimating divergence between two point processes, i.e. probability laws on the space of spike trains, is an essential tool in many computational neuroscience applications, such as change detection and neural coding. However, the prob-lem of estimating divergence, although well studied in the Euclidean space, has seldom been addressed in a more gen-eral setting. Since the space of spike trains can be viewed as a metric space, we address the problem of estimating Jensen-Shannon divergence in a metric space using a nearest neigh-bor based approach. We empirically demonstrate the validity of the proposed estimator, and compare it against other avail-able methods in the context of two-sample problem. Index Terms — Divergence, metric space, point process, nearest neighbor, hypothesis testing 1.
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"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
