Liquid Computing in a Simplified Model of Cortical Layer IV: Learning
to Balance a Ball
D. Probst, W. Maass, H. Markram, and M. O. Gewaltig
We present a biologically inspired recurrent network of spiking neurons and a
learning rule that enables the network to balance a ball on a flat circular
arena and to steer it towards a target field, by controlling the inclination
angles of the arena. The neural controller is a recurrent network of adaptive
exponential integrate and fire neurons configured and connected to match
properties of cortical layer IV. The network is used as a liquid state
machine with four action cells as readout neurons. The solution of the task
requires the controller to take its own reaction time into account by
anticipating the future state of the controlled system. We demonstrate that
the cortical network can robustly learn this task using a supervised learning
rule that penalizes the error on the force applied to the arena
Reference: D. Probst, W. Maass, H. Markram, and M. O. Gewaltig.
Liquid computing in a simplified model of cortical layer IV: Learning to
balance a ball.
In A. E. Villa, W. Duch, P. Erdi, F. Masulli, and G. Palm, editors, Proceedings of the 22nd International Conference on Artificial Neural
Networks and Machine Learning - ICANN 2012, volume 7552 of Lecture
Notes in Computer Science, pages 209-216. Springer, 2012.