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In this dissertation I present SNS-Toolbox, an open-source software package for the design and simulation of networks of biologically inspired neurons and synapses, also known as synthetic nervous systems (SNS). SNS-Toolbox allows SNS networks to be designed using a lightweight Python API, simulated in real-time on consumer computer hardware, and executed onboard physical robotic systems. I also present a companion package to SNS-Toolbox which allows simulation and training of large SNS networks using gradient backpropagation. This software is released under an open-source license with online documentation for ease of use, and has been disseminated to other researchers for their use. As a demonstration, I use SNS-Toolbox to implement a stereo visual motion detector, based on circuitry present within the Drosophila melanogaster (fruit fly) optic lobe. This network analyzes local motion at each point within a visual field, and returns an estimate of global motion when subjected to grating stimuli. Finally I showcase the design of FlyWheel, a robotic benchmark for studying models of insect vision and applying SNS networks to physical hardware. This body of work marks the first tool which is capable of simulating SNS networks with hundreds to thousands of neurons and synaptic connections in real-time or faster, optimize networks with chemical reversal potentials using gradient backpropagation, and interface these networks for control of external systems. 

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. 

This work focuses on creating a controller for the hip joint of a rat using a canonical motor microcircuit. It is thought that this circuit acts to modulate motor neuron activity at the output stage. We first created a simplified biomechanical model of a rat hindlimb along with a neural model of the circuit in a software tool called Animatlab. The canonical motor microcircuit controller was then tuned such that the trajectory of the hip joint was similar to that of a rat during locomotion. This work describes a successful method for hand-tuning the various synaptic parameters and the influence of Ia feedback on motor neuron activity. The neuromechanical model will allow for further analysis of the circuit, specifically, the function and significance of Ia feedback and Renshaw cells. 

This paper details the development and analysis of a computational neuroscience model, known as a Synthetic Nervous System, for the control of a simulated worm robot. Using a Synthetic Nervous System controller allows for adaptability of the network with minimal changes to the system. The worm robot kinematics are inspired by earthworm peristalsis which relies on the hydrostatic properties of the worm’s body to produce soft-bodied locomotion. In this paper the hydrostatic worm body is approximated as a chain of two dimensional rhombus shaped segments. Each segment has rigid side lengths, joints at the vertices, and a linear actuator to control the segment geometry. The control network is composed of non-spiking neuron and synapse models. It utilizes central pattern generators, coupled via interneurons and sensory feedback, to coordinate segment contractions and produce a peristaltic waveform that propagates down the body of the robot. A direct perturbation Floquet multiplier analysis was performed to analyze the stability of the peristaltic wave’s limit cycle. 

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. 

Leg coordination is important for walking robots. Insects are able to effectively walk despite having small metabolisms and size, and understanding the neural mechanisms which govern their walking could prove useful for improving legged robots. In order to explore the possible neural systems responsible for inter-leg coordination, leg positional data for walking fruit flies of the species Drosophila melanogaster was recorded, where one individual leg was amputated at the base of the tibia. These experiments have shown that when amputated, the remaining stump oscillates in a speed-dependent manner. At low walking speeds there is a wide range of possible stump periods, and this variance collapses down to a minimum as walking speed increases. We believe this behavior can be explained by noisy pattern generation networks (CPGs) within the legs, with intra-leg load feedback and inter-leg global signals stabilizing the network. In this paper, this biological data will be analyzed so that a simplified neuromechanical model can be designed in order to explain this behavior.