Name
Abstract | ||
|---|---|---|
We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and accelerati on, while the system's input are the instantaneous spike rates. The syste m's state dy- namics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity -to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajecto ries on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a stati c approach based on support vector regression and the Kalman filter. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | brain computer interfaces,support vector regression,kalman filter,loss function |
Field | DocType | Citations |
Kernel (linear algebra),Computer vision,Computer science,Support vector machine,Neural activity,Kalman filter,Artificial intelligence,Linear map,Acceleration,Discriminative model,Machine learning | Conference | 4 |
PageRank | References | Authors |
0.87 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shpigelman, Lavi | 1 | 68 | 7.09 |
| Koby Crammer | 2 | 5252 | 466.86 |
| Paz, Rony | 3 | 44 | 4.59 |
| Vaadia, Eilon | 4 | 141 | 15.90 |
| Y Singer | 5 | 13455 | 1559.02 |