Name
Abstract | ||
|---|---|---|
Modeling the brain has been a major scientific endeavor in the last few decades. Models can be at different levels of abstraction like chemical, cellular, region etc. Limitations in physical experimental setup will create poor model, if a regular pattern classifier system is used. This paper proposes a pattern recognition system to classify/predict animal behavior based on hippocampal signals. This system, takes into account the practical constraints during acquisition of the neural signal. The system performance for action classification is tested using multiple databases and percentage misclassification as low as 3.25% was obtained. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.neucom.2012.05.006 | Neurocomputing |
Keywords | Field | DocType |
neural signal,different level,behavior prediction,system performance,major scientific endeavor,hippocampal signal,regular pattern classifier system,animal behavior,pattern recognition system,multiple databases,action classification,hippocampus | Abstraction,Pattern recognition,Computer science,Animal behavior,Artificial intelligence,Classifier (linguistics),Pattern recognition system,Machine learning | Journal |
Volume | ISSN | Citations |
97, | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob Mathew | 1 | 0 | 0.34 |
| Laxmikanta Sahoo | 2 | 0 | 0.34 |
| Goutam Saha | 3 | 255 | 23.17 |