Semester
Fall
Date of Graduation
2023
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Nicholas Szczecinski
Committee Member
Sergiy Yakovenko
Committee Member
Xi Yu
Abstract
In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used to tune the conductances between the compartments (Szczecinski et al., 2017). Also, the Product of Exponentials (POE) was used to calculate the spatial kinematic chain of the stick insect leg (Murray et al., 1994). The system accurately encodes the foot position and depends on the width of the sensory encoding curves, or the “bell curves”. Discussion of limitations and other studies that relate to this work, as well as motivation for future work are included.
Recommended Citation
Guie, Chloe K., "Spiking Neural Network that Maps from Generalized Coordinates to Cartesian Coordinates" (2023). Graduate Theses, Dissertations, and Problem Reports. 12216.
https://researchrepository.wvu.edu/etd/12216