Name
Abstract | ||
|---|---|---|
This paper describes how Echo State Networks (ESN) can be used in conjunction with Minimum Average Correlation Energy (MACE) filters in order to create a system that can identify spikes in neural recordings. Various experiments using real-world data were used to compare the performance of the ESN-MACE against threshold and matched filter detectors to ascertain the capabilities of such a system in detecting neural action potentials. The experiments demonstrate that the ESN-MACE can correctly detect spikes with lower false alarm rates than established detection techniques since it captures the inherent variability and the covariance information in spike shapes by training. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371316 | 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 |
Keywords | Field | DocType |
neural nets,neurophysiology,false alarm rate,matched filter,statistical analysis,associative memory,action potential,echo state network | Content-addressable memory,False alarm,Computer science,Artificial intelligence,Matched filter,Artificial neural network,Detector,Covariance,Pattern recognition,Neurophysiology,Speech recognition,Correlation,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 2 | 0.39 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas J. Dedual | 1 | 6 | 0.87 |
| Mustafa C. Ozturk | 2 | 118 | 7.90 |
| Justin C. Sanchez | 3 | 176 | 28.68 |
| Jose C. Principe | 4 | 2295 | 282.29 |