Name
Abstract | ||
|---|---|---|
We introduce a visual analytics tool for neuroscience-inspired dynamic architectures (NIDA), a network type that has been previously shown to perform well on control, anomaly detection, and classification tasks. NIDA networks are a type of spiking neural network, a non-traditional network type that captures dynamics throughout the network. We demonstrate the utility of our visualization tool in exploring and understanding the structure and activity of NIDA networks. Finally, we describe several extensions to the visual analytics tool that will further aid in the development and improvement of NIDA networks and their associated design method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FOCI.2014.7007814 | Foundations of Computational Intelligence |
Keywords | Field | DocType |
data analysis,data visualisation,neural nets,pattern classification,NIDA networks,anomaly detection,classification tasks,design method,neuroscience-inspired dynamic architectures,nontraditional network type,spiking neural network,visual analytic tool,visualization tool | Anomaly detection,Computer science,Visualization,Visual analytics,Artificial intelligence,Spiking neural network,Artificial neural network,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.38 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Margaret Drouhard | 1 | 12 | 2.60 |
| Catherine D. Schuman | 2 | 170 | 21.91 |
| J. Douglas Birdwell | 3 | 59 | 10.38 |
| Mark E. Dean | 4 | 29 | 2.69 |