UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Inferring neural firing rates from spike trains using Gaussian Processes

Cunningham, JP; Yu, BM; Shenoy, KV; Sahani, M; (2009) Inferring neural firing rates from spike trains using Gaussian Processes. In:

Full text not available from this repository.

Abstract

Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of the spike train's underlying firing rate. Current techniques to find time-varying firing rates require ad hoc choices of parameters, offer no confidence intervals on their estimates, and can obscure potentially important single trial variability. We present a new method, based on a Gaussian Process prior, for inferring probabilistically optimal estimates of firing rate functions underlying single or multiple neural spike trains. We test the performance of the method on simulated data and experimentally gathered neural spike trains, and we demonstrate improvements over conventional estimators.

Type: Proceedings paper
Title: Inferring neural firing rates from spike trains using Gaussian Processes
ISBN: 160560352X
URI: http://discovery.ucl.ac.uk/id/eprint/120893
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item