{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/13739"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/13739","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"Feedback control of analog neurons and shunting inhibition in dendrites for bio-inspired neuromorphic systems.","abstract":"Neuromorphic engineering is an interdisciplinary field that takes inspiration from biological nervous systems to develop artificial computational architectures. This research revisits the original vision of neuromorphic computing by focusing on analog circuit implementations that closely emulate biological neural mechanisms. The work explores a tuning approach for analog neuron circuits based on the Izhikevich model and advances the use of dendritic circuits implemented with subthreshold MOSFET transistors. A key contribution is the development of a calibration technique for an analog neuron circuit capable of exhibiting complex spiking behaviors. A proof-of-concept tuning algorithm is introduced, leveraging spike pattern feature extraction and proportional feedback control to adjust the neuron’s output. This approach can be used to ensure consistent spiking responses across neurons in a neuromorphic system, even in the presence of noise or component variations. Another key contribution is the implementation of analog dendrite circuits as active computational elements rather than passive structures. By utilizing shunting inhibition— an inhibitory mechanism that modulates the response of a neuron’s membrane potential— an analog dendrite circuit is proposed that can perform gain modulation in a power-efficient manner. This is demonstrated through an analog circuit that models shunting inhibition and its effect on processing excitatory current signals. The research further investigates the potential of using analog dendrites in motion vision applications. Inspired by recent findings in Drosophila T4 neurons, an analog circuit using shunting inhibition is shown to enable high-level computation like coincidence detection and direction selectivity. This approach offers an alternative to classical delay-and-compare models, potentially overcoming their limitations in processing high-speed stimuli. The adaptability of the shunting conductance mechanism could allow for a broader range of velocity tuning in neuromorphic vision systems. Ultimately, this work bridges gaps in analog neuromorphic system design by introducing neuron calibration techniques and demonstrating an application of active dendritic computation. The findings contribute to the broader goal of developing biologically inspired, energy-efficient computing architectures that leverage the intricate computational properties of neural tissue.","abstract_html":"Neuromorphic engineering is an interdisciplinary field that takes inspiration from biological nervous systems to develop artificial computational architectures. This research revisits the original vision of neuromorphic computing by focusing on analog circuit implementations that closely emulate biological neural mechanisms. The work explores a tuning approach for analog neuron circuits based on the Izhikevich model and advances the use of dendritic circuits implemented with subthreshold MOSFET transistors. A key contribution is the development of a calibration technique for an analog neuron circuit capable of exhibiting complex spiking behaviors. A proof-of-concept tuning algorithm is introduced, leveraging spike pattern feature extraction and proportional feedback control to adjust the neuron’s output. This approach can be used to ensure consistent spiking responses across neurons in a neuromorphic system, even in the presence of noise or component variations. Another key contribution is the implementation of analog dendrite circuits as active computational elements rather than passive structures. By utilizing shunting inhibition— an inhibitory mechanism that modulates the response of a neuron’s membrane potential— an analog dendrite circuit is proposed that can perform gain modulation in a power-efficient manner. This is demonstrated through an analog circuit that models shunting inhibition and its effect on processing excitatory current signals. The research further investigates the potential of using analog dendrites in motion vision applications. Inspired by recent findings in Drosophila T4 neurons, an analog circuit using shunting inhibition is shown to enable high-level computation like coincidence detection and direction selectivity. This approach offers an alternative to classical delay-and-compare models, potentially overcoming their limitations in processing high-speed stimuli. The adaptability of the shunting conductance mechanism could allow for a broader range of velocity tuning in neuromorphic vision systems. Ultimately, this work bridges gaps in analog neuromorphic system design by introducing neuron calibration techniques and demonstrating an application of active dendritic computation. The findings contribute to the broader goal of developing biologically inspired, energy-efficient computing architectures that leverage the intricate computational properties of neural tissue.","abstract_has_math":false,"creators":["Parker, Luke Garrison, 1996-"],"institution":"Baylor University.","degree_name":"Ph.D.","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Koziol, Scott M."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-24T01:07:58Z","subjects":["Neuromorphic computing.","Bio-inspired systems.","Analog circuits.","Neural networks.","Dendrites.","Neurons."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2104/13739"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Neuromorphic engineering is an interdisciplinary field that takes inspiration from biological nervous systems to develop artificial computational architectures. This research revisits the original vision of neuromorphic computing by focusing on analog circuit implementations that closely emulate biological neural mechanisms. The work explores a tuning approach for analog neuron circuits based on the Izhikevich model and advances the use of dendritic circuits implemented with subthreshold MOSFET transistors. A key contribution is the development of a calibration technique for an analog neuron circuit capable of exhibiting complex spiking behaviors. A proof-of-concept tuning algorithm is introduced, leveraging spike pattern feature extraction and proportional feedback control to adjust the neuron’s output. This approach can be used to ensure consistent spiking responses across neurons in a neuromorphic system, even in the presence of noise or component variations. Another key contribution is the implementation of analog dendrite circuits as active computational elements rather than passive structures. By utilizing shunting inhibition— an inhibitory mechanism that modulates the response of a neuron’s membrane potential— an analog dendrite circuit is proposed that can perform gain modulation in a power-efficient manner. This is demonstrated through an analog circuit that models shunting inhibition and its effect on processing excitatory current signals. The research further investigates the potential of using analog dendrites in motion vision applications. Inspired by recent findings in Drosophila T4 neurons, an analog circuit using shunting inhibition is shown to enable high-level computation like coincidence detection and direction selectivity. This approach offers an alternative to classical delay-and-compare models, potentially overcoming their limitations in processing high-speed stimuli. The adaptability of the shunting conductance mechanism could allow for a broader range of velocity tuning in neuromorphic vision systems. Ultimately, this work bridges gaps in analog neuromorphic system design by introducing neuron calibration techniques and demonstrating an application of active dendritic computation. The findings contribute to the broader goal of developing biologically inspired, energy-efficient computing architectures that leverage the intricate computational properties of neural tissue."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Feedback control of analog neurons and shunting inhibition in dendrites for bio-inspired neuromorphic systems."]}]}],"canonical_facts":{"dc:contributor.advisor":["Koziol, Scott M."],"dc:creator":["Parker, Luke Garrison, 1996-"],"dc:date.accessioned":["2025-09-05T15:11:36Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Neuromorphic engineering is an interdisciplinary field that takes inspiration from biological nervous systems to develop artificial computational architectures. This research revisits the original vision of neuromorphic computing by focusing on analog circuit implementations that closely emulate biological neural mechanisms. The work explores a tuning approach for analog neuron circuits based on the Izhikevich model and advances the use of dendritic circuits implemented with subthreshold MOSFET transistors. A key contribution is the development of a calibration technique for an analog neuron circuit capable of exhibiting complex spiking behaviors. A proof-of-concept tuning algorithm is introduced, leveraging spike pattern feature extraction and proportional feedback control to adjust the neuron’s output. This approach can be used to ensure consistent spiking responses across neurons in a neuromorphic system, even in the presence of noise or component variations. Another key contribution is the implementation of analog dendrite circuits as active computational elements rather than passive structures. By utilizing shunting inhibition— an inhibitory mechanism that modulates the response of a neuron’s membrane potential— an analog dendrite circuit is proposed that can perform gain modulation in a power-efficient manner. This is demonstrated through an analog circuit that models shunting inhibition and its effect on processing excitatory current signals. The research further investigates the potential of using analog dendrites in motion vision applications. Inspired by recent findings in Drosophila T4 neurons, an analog circuit using shunting inhibition is shown to enable high-level computation like coincidence detection and direction selectivity. This approach offers an alternative to classical delay-and-compare models, potentially overcoming their limitations in processing high-speed stimuli. The adaptability of the shunting conductance mechanism could allow for a broader range of velocity tuning in neuromorphic vision systems. Ultimately, this work bridges gaps in analog neuromorphic system design by introducing neuron calibration techniques and demonstrating an application of active dendritic computation. The findings contribute to the broader goal of developing biologically inspired, energy-efficient computing architectures that leverage the intricate computational properties of neural tissue."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/13739"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"dc:subject":["Neuromorphic computing.","Bio-inspired systems.","Analog circuits.","Neural networks.","Dendrites.","Neurons."],"dc:title":["Feedback control of analog neurons and shunting inhibition in dendrites for bio-inspired neuromorphic systems."],"dc:type":["Thesis"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["Baylor University."]},"updated_at":"2026-07-24T01:07:58Z"}