资源分类
》 资源 》论文》On the Analysis of Multi-Channel Neural Spike Data

On the Analysis of Multi-Channel Neural Spike Data

2020-01-08 | |  69 |   37 |   0

Abstract

Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly. The feature learning and sorting are performed simultaneously across all channels. Dictionary learning is implemented via the beta-Bernoulli process, with spike sorting performed via the dynamic hierarchical Dirichlet process (dHDP), with these two models coupled. The dHDP is augmented to eliminate refractoryperiod violations, it allows the “appearance” and “disappearance” of neurons over time, and it models smooth variation in the spike statistics.

上一篇：Prediction strategies without loss

下一篇：Beating SGD: Learning SVMs in Sublinear Time

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...