Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Abstract

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.

Integrating Non-Spiking Interneurons in Spiking Neural Networks

“…The sCPG populations and MNP consist of 5 neurons each in order to produce a smooth, stable output with a minimum amount of neurons (Strohmer et al, 2020). The NSI is a single neuron so that all post-synaptic neurons connected to it receive the same inputs.…”

“…Pure SNNs are also capable of manipulating output amplitude, frequency, and phase by updating differ-ent synaptic and neuronal characteristics (Strohmer et al, 2020). It was found that frequency could be changed by updating the value of the voltage threshold potential of the spiking central pattern generator (sCPG) neuron populations while amplitude was increased or decreased using the weight of the synap-tic conductance to the motor neuron population (MNP).…”

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

“…Similarly, when the module state is toggled by outside stimuli, its behavior may appear superficially similar to bursting, but the underlying dynamics are fundamentally different [ 19 ]. This mechanism of rhythmogenesis is certainly not typical for biological central pattern generation [ 11 ], but it is effective from an engineering perspective.…”

“…Instead, they simply provide contractile force in proportion to their depolarization [ 27 ]. The result is similar to applying an exponential low-pass filter to the spiking waveform [ 11 ], smoothing the output of the system so that changes occur only on the timescale of a few milliseconds, determined by the synaptic and membrane time constants.…”

“…Applications of this principle to robotics include a variety of open-loop spiking CPGs generating position commands for robots ranging from hexapods [11] to fish [12]. Also, certain researchers have decided to tackle the problem of proprioceptive feedback in quadrupeds [13][14][15].…”