A0559
Title: Neural decoding based on an infinite mixture model
Authors: Ryohei Shibue - NTT Communication Science Laboratories (Japan) [presenting]
Fumiyasu Komaki - RIKEN CBS (Japan)
Abstract: Neural decoding is a framework for reconstructing external stimuli from spike trains recorded by various neural recordings. We propose a neural decoding method based on an infinite mixture model and Bayesian nonparametrics. The proposed method improves decoding performance in terms of accuracy and computational speed. We apply the proposed method to simulation and experimental data to verify its performance.
