Towards bioinspired close-loop local motor control: a simulated approach supporting neuromorphic implementations

Despite being well established in robotics, classical motor controllers have several disadvantages: they pose a high computational load, therefore requiring powerful devices, they are not easy to tune and they are not suited for neuroprosthetics. In contrast, bio-inspired controller do not transform the output of the controller therefore no delays are introduced and a smooth response is achieved; they also have a high scalability. Finally, the most important feature of bio-inspired controllers is that they could integrate learning features to make them adaptable to new tasks within the same hardware robotic platform. We present the model and simulation of a spiking neural network for low-level motor control. The proposed neural network acts as a motor controller and produces pulsed signals which can be directly interfaced with commercial DC motors. The simulated network is compatible with neuromorphic VLSI implementation and paves the way to the implementation bio-inspired motor controller which are compact, low power, scalable and compatible with neuroprosthetic. The network presented is inspired by the current knowledge about biological motor control: it comprises alpha motoneuron for driving the motor and spindle populations to provide the feedback and close the loop. The spikes from the motoneuron population are time lengthen to a fixed amount of time and supplied to the simulated motor: Pulse Frequency Modulation (PFM) modulation is used. This paper presents the software simulations using the Brian simulator for a position controller. Our controller is a first step toward a novel bioinspired motor control approach suitable for robotics as well as neuroprosthetic.