Neuromorphic Implementation of Spiking Relational Neural Network for Motor Control

Abstract

Despite the rapid development of robotic control theory, hardware motor controllers still suffer from some disadvantages: they are computationally-intensive and rely on powerful computing systems which are usually implemented using bulky and power-hungry devices. On the other hand, biological motor control systems are power-efficient, light-weight and robust. Neuromorphic engineering sheds a light on how to uncover biological control features that could lead to the design of lower power and less bulky controllers. In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network. The network performs PI control by encoding target, feedback and error signals using population coding. It continuously calculates the error through the network, which relates the three variables by means of feed-forward inter-population synapses. This biologically plausible and fault-tolerant strategy is ideally suited for the use of neuromorphic hardware that comprises noisy silicon neurons. Here we show how to optimize the network structure to make it robust to both noisy inputs and device mismatch. We provide experimental results showing how the controller can reach 97.1% accuracy with 75.8 ms average latency.