Latent variable models for neural data analysis
Latent variable models for neural data analysis
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
A Method for Selecting the Bin Size of a Time Histogram
Neural Computation
Fast Gaussian process methods for point process intensity estimation
Proceedings of the 25th international conference on Machine learning
Editorial: Recent advances in brain-machine interfaces
Neural Networks
Detection of hidden structures in nonstationary spike trains
Neural Computation
Optimizing time histograms for non-poissonian spike trains
Neural Computation
Information transmission using non-poisson regular firing
Neural Computation
Hi-index | 0.00 |
Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.