First Advisor

Alex Hunt

Term of Graduation

Fall 2025

Date of Publication

12-8-2025

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Mechanical Engineering

Department

Mechanical and Materials Engineering

Language

English

Subjects

Ia Sensory Neurons, Neural Networks, Proprioceptive Feedback, Spike Timing Dependent Plasticity, Synthetic Nervous System, Unsupervised Learning

Physical Description

1 online resource (x, 84 pages)

Abstract

This study investigates how type Ia feedback from muscle spindles can be organized into groups representing agonistic muscle pairs through Spike Timing Dependent Plasticity (STDP). A single degree of freedom joint is actuated with four biologically modeled muscles forming two agonistic pairs. In order to emulate the sensory dynamics of biological muscle spindles, sensors in the model record the active length and velocity states of each muscle, the two primary factors eliciting type Ia afferent responses. In biological networks, synapses from Ia sensory neurons frequently activate interneurons representing agonistic muscle sources. This research investigates whether this organization can emerge in an initially unsorted network through synaptic modulation via STDP. The network of interconnected Ia sensory neurons and interneurons initiates with random conductance values, without knowledge of the desired organization structure. Under semi-randomized muscle activation, STDP in the proprioceptive network demonstrates the ability to organize Ia sensory neuron signals into groups according to their agonistic sources, mirroring known architecture. STDP provides a biologically plausible, unsupervised learning mechanism by which the known connections in vivo may form. With continuing work, STDP may prove itself capable of organizing proprioceptive networks for larger musculoskeletal systems, possibly on the scale of biological creatures. Future investigations will explore how the application of STDP to the other known synaptic connections in the Ia afferent network may assist in additional organization of the network architecture.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Comments

This work was supported by NSF DBI 2015317 as part of the NSF/CIHR/DFG/FRQ/ UKRI-MRC Next Generation Networks for Neuroscience Program.

Persistent Identifier

https://archives.pdx.edu/ds/psu/44400

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