Name
Abstract | ||
|---|---|---|
Neuronal response latency is usually vaguely defined as the delay between the stimulus onset and the beginning of the response. It contains important information for the understanding of the temporal code. For this reason, the detection of the response latency has been extensively studied in the last twenty years, yielding different estimation methods. They can be divided into two classes, one of them including methods based on detecting an intensity change in the firing rate profile after the stimulus onset and the other containing methods based on detection of spikes evoked by the stimulation using interspike intervals and spike times. The aim of this paper is to present a review of the main techniques proposed in both classes, highlighting their advantages and shortcomings. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.biosystems.2015.04.008 | Biosystems |
Keywords | Field | DocType |
Change point analysis,Evoked activity,Maximum likelihood estimation,Bayesian analysis,Spike trains,Extracellular recordings in neurons | Latency (engineering),Computer science,Maximum likelihood,Artificial intelligence,Stimulus (physiology),Intensity change,Evoked activity,Latency estimation,Machine learning | Journal |
Volume | ISSN | Citations |
136 | 0303-2647 | 4 |
PageRank | References | Authors |
0.41 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marie Levakova | 1 | 10 | 1.88 |
| M Tamborrino | 2 | 11 | 2.24 |
| Susanne Ditlevsen | 3 | 57 | 7.84 |
| P Lansky | 4 | 9 | 1.51 |