Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications

Zeglen, Matthew Aaron
http://orcid.org/0000-0002-7249-4148
http://rave.ohiolink.edu/etdc/view?acc_num=wright1653657136496514

Abstract Details

2022, Master of Science in Electrical Engineering (MSEE), Wright State University, Electrical Engineering.
Cognitive capabilities for robotic applications are furthered by developing an artificial amygdala that mimics biology. The amygdala portion of the brain is commonly understood to control mood and behavior based upon sensory inputs, motivation, and context. This research builds upon prior work in creating artificial intelligence for robotics which focused on mood-generated actions. However, recent amygdala research suggests a void in greater functionality. This work developed a computational model of an amygdala, integrated this model into a robot model, and developed a comprehensive integration of the robot for simulation, and live embodiment. The developed amygdala, instantiated in the Nengo Brain Maker environment, leveraged spiking neural networks and the semantic pointer architecture to allow the abstraction of neuron ensembles into high-level concept vocabularies. Test and validation were performed on a TurtleBot in both simulated (Gazebo) and live testing. Results were compared to a baseline model which has a simplistic, amygdala-like model. Metrics of nearest distance and nearest time were used for assessment. The amygdala model is shown to outperform the baseline in both simulations, with a 70.8% improvement in nearest distance and, 4% improvement in the nearest time, and in real applications with a 62.4% improvement in nearest distance. Notably, this performance occurred despite a five-fold increase in architecture size and complexity.
Luther Palmer III, Ph.D. (Advisor)
Trevor J. Bihl, Ph.D. (Committee Member)
Xiaodong Zhang, Ph.D. (Committee Member)
141 p.
Artificial Intelligence; Electrical Engineering; Robotics
Artificial Intelligence; A.I.; SNN; Spiking Neural Network; Robot; Robotics; Cognition; Amygdala; Artificial Cognition; ROS; Gazebo; Nengo

Recommended Citations

Citations

  • Zeglen, M. A. (2022). Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1653657136496514

    APA Style (7th edition)

  • Zeglen, Matthew. Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications. 2022. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1653657136496514.

    MLA Style (8th edition)

  • Zeglen, Matthew. "Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications." Master's thesis, Wright State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=wright1653657136496514

    Chicago Manual of Style (17th edition)

wright1653657136496514
222
© 2022, some rights reserved.Creative Commons License Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications by Matthew Aaron Zeglen is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.