In this work, we present a spiking neural network (SNN) based PID controller on a neuromorphic chip. On-chip SNNs are currently being explored in low-power AI applications. Due to potentially ultra-low power consumption, low latency, and high processing speed, on-chip SNNs are a promising tool for control of power-constrained platforms, such as Unmanned Aerial Vehicles (UAV). To obtain highly efficient and fast end-toend neuromorphic controllers, the SNN-based AI architectures must be seamlessly integrated with motor control. Towards this goal, we present here the first implementation of a fully neuromorphic PID controller. We interfaced Intel's neuromorphic research chip Loihi to a UAV, constrained to a single degree of freedom. We developed an SNN control architecture using populations of spiking neurons, in which each spike carries information about the measured, control, or error value, defined by the identity of the spiking neuron. Using this sparse code, we realize a precise PID controller. The P, I, and D gains of the controller are implemented as synaptic weights that can adapt according to an on-chip plasticity rule. In future work, these plastic synapses can be used to tune and adapt the controller autonomously.
Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.
Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.
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