Name
Abstract | ||
|---|---|---|
Amplitude thresholds are used in extracting information from extracellular neural recordings. While the amplitude threshold may be estimated by ensuring that behavioral variables are encoded in neural activity with maximum signal-to-noise ratio, it can also be estimated by maximum likelihood. Here, these two types of threshold are estimated using extracellular recordings collected from the motor cortex (M1) of two rats performing a well-learned visuomotor task and the estimates are compared. The results show that these two types of threshold are not significantly different. On the other hand, the width of the confidence interval of the maximum likelihood estimate is found to be related to decoding accuracy. These results are important for determining appropriate amplitude thresholds to be used in brain-machine interfaces. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SIU.2019.8806588 | 2019 27th Signal Processing and Communications Applications Conference (SIU) |
Keywords | Field | DocType |
computational neuroscience,spike train analysis,generalized linear models | Computational neuroscience,Pattern recognition,Computer science,Maximum likelihood,Generalized linear model,Extracellular,Artificial intelligence,Motor cortex,Decoding methods,Confidence interval,Amplitude | Conference |
ISSN | ISBN | Citations |
2165-0608 | 978-1-7281-1905-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eda Dagdevir | 1 | 0 | 0.68 |
| Mehmet Kocatürk | 2 | 0 | 0.68 |
| Murat Okatan | 3 | 0 | 0.68 |