Paper Info
Title
Spike train analysis for single trial data
Abstract
Traditional methods in neural data analysis are not appropriate for analyzing the spike train of a single experimental trial. We show that, by constructing a model of firing statistics, a more accurate estimate of the firing rate for a single spike train can be obtained. The model is based on the assumption that the neuron's spikes are generated by a non-homogeneous Poisson process which follows Markovian dynamics. We test the method by reconstructing the input stimulus based on the neurons’ responses either on the raw spike data or the firing rate estimate. The spike data were recorded from macaque V1 neurons in response to a sinewave grating undergoing pseudo-random walk. For a large percentage of the cells studied, the reconstruction is significantly improved by using the estimated firing rate over the raw spikes, suggesting that estimated rate reflects more accurately the underlying state of the neurons.
Year
DOI
Venue
2002
10.1016/S0925-2312(02)00446-0
Neurocomputing
Keywords
Field
DocType
V1 neurons,HMM model,Firing rate estimation,Stimulus reconstruction
Grating,Markov process,Macaque,Spike train,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Stimulus (physiology),Poisson process,Machine learning,Sine wave
Journal
Volume
ISSN
Citations 
44
0925-2312
2
PageRank 
References 
Authors
0.55
1
2
Name
Order
Citations
PageRank
Richard Romero171.57
Tai Sing Lee279488.73