Name
Abstract | ||
|---|---|---|
The present article proposes a novel computational approach to the motor planning. In this approach, each motor command is represented as a linear combination of prefixed basis patterns, and the command for a given task is designed by minimizing a two-termed criterion consisting of a task optimization term and a parameter preference (i.e., sparseness) term. The result of a computer simulation with a single-joint reaching task confirmed that our ''representation-based'' criterion for motor planning appropriately worked, together with showing that the resultant trajectory qualitatively replicated Fitts' law. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neucom.2006.10.120 | Neurocomputing |
Keywords | Field | DocType |
linear combination,present article,para- metric command representation,task optimization term,sparse representation,computer simulation,parameter preference,prefixed basis pattern,motor command,sparse motor command representation,novel computational approach,two-termed criterion,motor planning | Linear combination,Computer science,Motor planning,Sparse approximation,Artificial intelligence,Machine learning,Trajectory | Journal |
Volume | Issue | ISSN |
70 | 10-12 | Neurocomputing |
Citations | PageRank | References |
3 | 0.53 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutaka Sakaguchi | 1 | 26 | 7.81 |
| Shiro Ikeda | 2 | 344 | 37.95 |