Name
Abstract | ||
|---|---|---|
Learning weights in a spiking neural network with hidden neurons, using local, stable and online rules, to control non-linear body dynamics is an open problem. Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden layers, to control a two-link arm so as to reproduce a desired state trajectory. The network first learns an inverse model of the non-linear dynamics, i.e. from state trajectory as input to the network, it learns to infer the continuous-time command that produced the trajectory. Connection weights are adjusted via a local plasticity rule that involves pre-synaptic firing and post-synaptic feedback of the error in the inferred command. We choose a network architecture, termed differential feedforward, that gives the lowest test error from different feedforward and recurrent architectures. The learned inverse model is then used to generate a continuous-time motor command to control the arm, given a desired trajectory. |
Year | Venue | DocType |
|---|---|---|
2018 | international conference on machine learning | Conference |
Volume | ISSN | Citations |
abs/1712.10158 | Proceedings of the 35th International Conference on Machine
Learning, PMLR 80:1773-1782, 2018 | 0 |
PageRank | References | Authors |
0.34 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Gilra | 1 | 0 | 1.35 |
| Wulfram Gerstner | 2 | 2437 | 410.08 |