Spiking Neurons Ensemble for Movement Generation in Dynamically Changing Environments

Spiking neurons might play a larger role than simply as an efficient signal transmitter. Several studies have demonstrated how movements can be generated using networks of spiking neurons. However, the complexity of spiking neural networks makes their implementation difficult, and the use of spiking neurons in robotics has remained largely impractical. In this paper, we show that the addition of a single layer of spiking neurons can help improve performance on stabilization tasks in dynamically changing environments. In a one-dimensional inverted pendulum stabilization task, the spiking neurons seem to expand the space of usable parameters of the controller. Using a robot arm in 3-D space, the additional layer of spiking neurons suffices to improve performance up to 30% on an inverted pendulum stabilization task. We expect this technique to enhance performance in most stabilization tasks but also tasks that are essentially similar such as reaching tasks and posture control. We also expect the effects of this layer to be greatest when the optimal tuning of control parameters is difficult, such as when the environment is unpredictable and dynamic.