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Aronhalt, E., Abramson, E., Goldsmith, C., Andrada, E., Nourse, W., Sutton, G., Szczecinski, N. S., & Quinn, R. D. (2023, July). Development of a Robotic Rat Hindlimb Model. In Conference on Biomimetic and Biohybrid Systems (pp. 115-130). Cham: Springer Nature Switzerland. 

Nourse, W. R., Szczecinski, N. S., & Quinn, R. D. (2023, July). A Synthetic Nervous System for on and Off Motion Detection Inspired by the Drosophila melanogaster Optic Lobe. In Conference on Biomimetic and Biohybrid Systems (pp. 364-380). Cham: Springer Nature Switzerland. 

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control. 

We introduce SNS-Toolbox, a Python software package for the design and simulation of networks of conductance-based neurons and synapses, also called Synthetic Nervous Systems (SNS). SNS-Toolbox implements non-spiking and spiking neurons in multiple software backends, and is capable of simulating networks with thousands of neurons in real-time. We benchmark the toolbox simulation speed across multiple network sizes, characterize upper limits on network size in various scenarios, and showcase the design of a two-layer convolutional network inspired by circuits within the Drosophila melanogaster optic lobe. SNSToolbox, as well as the code to generate all of the figures in this work, is located at https://github.com/wnourse05/SNS-Toolbox. 

How we interact with our environment largely depends on both the external cues presented by our surroundings and the internal state from within. Internal states are the ever-changing physiological conditions that communicate the immediate survival needs and motivate the animal to behaviorally fulfill them. Satiety level constitutes such a state, and therefore has a dynamic influence on the output behaviors of an animal. In predatory insects like the praying mantis, hunting tactics, grooming, and mating have been shown to change hierarchical organization of behaviors depending on satiety. Here, we analyze behavior sequences of freely hunting praying mantises ( Tenodera sinensis ) to explore potential differences in sequential patterning of behavior as a correlate of satiety. First, our data supports previous work that showed starved praying mantises were not just more often attentive to prey, but also more often attentive to further prey. This was indicated by the increased time fraction spent in attentive bouts such as prey monitoring, head turns (to track prey), translations (closing the distance to the prey), and more strike attempts. With increasing satiety, praying mantises showed reduced time in these behaviors and exhibited them primarily towards close-proximity prey. Furthermore, our data demonstrates that during states of starvation, the praying mantis exhibits a stereotyped pattern of behavior that is highly motivated by prey capture. As satiety increased, the sequenced behaviors became more variable, indicating a shift away from the necessity of prey capture to more fluid presentations of behavior assembly.