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Robot Locomotion through Tunable Spiking and Bursting Rhythms using Efficient Bio-mimetic Neural Networks on Loihi and Arduino Platforms

Vivekanand, Vijay Shankaran (2024) Robot Locomotion through Tunable Spiking and Bursting Rhythms using Efficient Bio-mimetic Neural Networks on Loihi and Arduino Platforms. Master's Thesis, University of Pittsburgh. (Unpublished)

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Rhythmic tasks that biological beings perform such as breathing, walking, and swimming, use specialized neural networks called central pattern generators (CPG). This paper aims to take this concept further by designing and implementing a tunable bursting central pattern generator to control quadruped robots for the first time, to the best of our knowledge. Bursting CPGs allow for more granular control over the motion and speed of operation while retaining the low memory usage and latency capabilities of spiking CPGs. A bio-mimetic neuron model is chosen for this implementation which is highly optimized to run real-time on standard (Arduino microcontroller) and specialized (Intel Loihi) hardware. The Petoi bittle is chosen as the model hardware setup to showcase the efficiency of the proposed CPGs even in serial processing architectures. The CPG network is also realized in a completely asynchronous Loihi architecture to illustrate its versatility. The fully connected network running on CPG takes around 10 kilo bytes of memory (33% of Arduino capacity) to execute different modes of locomotion - walk, jump, trot, gallop, and crawl. Benchmarking results show that the bio-mimetic neurons take around 600 bytes (around 2%) more memory than Izhikevich neurons while being 0.02ms (around 14%) faster in isolated neuron testing.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Vivekanand, Vijay Shankaranv.vijayshankaran@gmail.comviv40@pitt.edu0009-0001-3631-674X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorKubendran, Rajkumarrajkumar.ece@pitt.edurak1960000-0003-3066-4898
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm40000-0002-3025-463X
Committee MemberCan-Cimino, Azimeazc9@pitt.eduazc90000-0003-1382-4466
Date: 11 January 2024
Date Type: Publication
Defense Date: November 2023
Approval Date: 11 January 2024
Submission Date: 26 October 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 35
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Bursting Central Pattern Generators, Neuromorphic Computing, Network Neuromodulation, Hebbian Learning, Dynamic Evolving State Machine
Date Deposited: 11 Jan 2024 19:40
Last Modified: 11 Jan 2024 19:40


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