Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data
This work addresses classification challenges in computational neuroscience and related fields, but it is incremental as it extends existing probabilistic methods to nonparametric specifications.
The paper tackles the two-class classification problem for spike train data with nonparametric intensity functions by deriving the optimal Bayes rule and a plug-in kernel classifier, proving its convergence to the Bayes rule with asymptotic properties and supporting results with simulation studies.
Spike trains data find a growing list of applications in computational neuroscience, imaging, streaming data and finance. Machine learning strategies for spike trains are based on various neural network and probabilistic models. The probabilistic approach is relying on parametric or nonparametric specifications of the underlying spike generation model. In this paper we consider the two-class statistical classification problem for a class of spike train data characterized by nonparametrically specified intensity functions. We derive the optimal Bayes rule and next form the plug-in nonparametric kernel classifier. Asymptotical properties of the rules are established including the limit with respect to the increasing recording time interval and the size of a training set. In particular the convergence of the kernel classifier to the Bayes rule is proved. The obtained results are supported by a finite sample simulation studies.