Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  test the performance of the method on simulated data and experimentally gathered neural spike trains, and  demonstrate improvements over conventional estimators.
['John P. Cunningham', 'Byron M. Yu', 'Krishna V. Shenoy', 'Maneesh Sahani']
I am writing a research paper on a idea Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
[{"from": "human", "value": "I am writing a research paper on a idea Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  test the performance of the method on simulated data and experimentally gathered neural spike trains, and  demonstrate improvements over conventional estimators."}]