A spiking central pattern generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards

Abstract

Central pattern generator (CPG) models have long been used to investigate both the neural mechanisms that underlie animal locomotion, as well as for robotic research. In this work we propose a spiking central pattern generator (SCPG) neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our SCPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the neural engineering framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the CPG model, the model can be turned into a spiking neural network (SNN) that can be easily simulated with Nengo, an SNN simulator. The SCPG model is then used to produce the swimming gaits of a simulated lamprey robot model in various scenarios. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. The proposed methodology can be generalized to other types of CPGs suitable for both engineering applications and scientific research. We test our system on two neuromorphic platforms, SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms displays a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

“…Progress in the physiological modeling of locomotor circuitry in the spinal cord and brainstem demonstrates the role of neural circuits in gait dynamics. However, these models typically rely on simplified (Dzeladini et al, 2014; Taga et al, 1991) biomechanical properties and cannot yet predict the deficits in gait specific to an individual (Angelidis et al, 2021; Geyer and Herr, 2010; Kuo, 2002; McCrea and Rybak, 2008). More importantly, if a hyper-realistic model of the neural and biomechanical system did exist, the relationships between the high-dimensional parameters and actual movement patterns would not likely be unique, as many parameters would not be identifiable, even given massive amounts of data, as many different parameter choices could lead to the same biomechanical output (Holmes et al, 2006; Prinz et al, 2004).…”

Spiking Neural Networks and Their Applications: A Review

“…Reference [ 83 ] proposed to implement a sCPG using an analog/digital VLSI device interfaced with an FPGA board, which can be directly interfaced to the actuators of a bio-mimetic robotic lamprey. Reference [ 84 ] used the sCPG model implemented in Nengo to produce the swimming gaits modulated by the high-level brainstem control of a simulated lamprey robot model in various scenarios. They showed that the robot can be controlled dynamically in direction and pace by modifying the input to the network.…”

A perspective on the neuromorphic control of legged locomotion in past, present, and future insect-like robots

“…This was followed by chips from IBM (Merolla et al 2014, Akopyan et al 2015 and Intel (Davies et al 2018(Davies et al , 2021, which were dedicated to simulating arbitrary networks of leaky integrate-and-fire spiking neurons. These digital processors have proved to be easier to manufacture than their analog predecessors, and due to their improved availability have been used in a variety of robotic applications (Davies et al 2021, Cohen 2022 including CPGs for legged locomotion of robots ((Gutierrez-Galan et al 2019); in simulation: (Polykretis et al 2020, Angelidis et al 2021). As the second generation of these chips, which are capable of simulating up to one million simple neurons per chip, start to become available (Orchard et al 2021, Yan et al 2022, we foresee practical neural control systems running on-board legged robots approaching the scale of those in arthropod nervous systems.…”