{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78735"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78735","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards a neocortically-inspired ab initio cellular model of associative memory","abstract":"We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio (simulated) spiking neural networks (SNNs) with cellular resolution and complex network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level measurements are information-preserving and could be used as the basis of an associative memory. The dissertation details the relevant background and results of a series of experiments designed to accomplish this objective. The results provide encouraging empirical evidence that such a model can be used for encoding attractors with multi-sensory inputs and across sensory modalities, which both emphasize the potential of such a model for use as a multi-modal associative memory.","abstract_html":"We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio (simulated) spiking neural networks (SNNs) with cellular resolution and complex network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level measurements are information-preserving and could be used as the basis of an associative memory. The dissertation details the relevant background and results of a series of experiments designed to accomplish this objective. The results provide encouraging empirical evidence that such a model can be used for encoding attractors with multi-sensory inputs and across sensory modalities, which both emphasize the potential of such a model for use as a multi-modal associative memory.","abstract_has_math":false,"creators":["Duda, Alexander Michael"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Levinson, Stephen E.","Baryshnikov, Yuliy","Milenkovic, Olgica","Rothganger, Fredrick"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:45:19Z","date_published":"2015-07-22T22:45:19Z","updated_at":"2026-07-22T22:26:12Z","subjects":["Ab Initio Cellular Models","Associative Memory","Attractors","Complex Networks","Emergence","Information-Preserving","Multi-Scale Modeling","Neurorobotics","Nonlinear Dynamics","Real-World Coupling","Spiking Neural Networks","Spike-timing Dependent Plasticity","Spike-timing Dependent Plasticity (STDP) Learning","Topological Adaptation"],"languages":["en"],"rights":["Copyright 2015 Alexander Duda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78735","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Levinson, Stephen E.","Baryshnikov, Yuliy","Milenkovic, Olgica","Rothganger, Fredrick"]},{"key":"dc:creator","label":"Author","values":["Duda, Alexander Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:45:19Z","2017-07-23T09:15:34Z","2015-05","2015-04-17","2015-5"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ab Initio Cellular Models","Associative Memory","Attractors","Complex Networks","Emergence","Information-Preserving","Multi-Scale Modeling","Neurorobotics","Nonlinear Dynamics","Real-World Coupling","Spiking Neural Networks","Spike-timing Dependent Plasticity","Spike-timing Dependent Plasticity (STDP) Learning","Topological Adaptation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Alexander Duda"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78735"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio (simulated) spiking neural networks (SNNs) with cellular resolution and complex network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level measurements are information-preserving and could be used as the basis of an associative memory. The dissertation details the relevant background and results of a series of experiments designed to accomplish this objective. The results provide encouraging empirical evidence that such a model can be used for encoding attractors with multi-sensory inputs and across sensory modalities, which both emphasize the potential of such a model for use as a multi-modal associative memory.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-05-01","The student, Alexander Duda, accepted the attached license on 2015-04-15 at 10:36.","The student, Alexander Duda, submitted this Dissertation for approval on 2015-04-15 at 11:18.","This Dissertation was approved for publication on 2015-04-17 at 11:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7863 on 2015-07-22 at 14:24:27","Made available in DSpace on 2015-07-22T22:45:19Z (GMT). No. of bitstreams: 2 DUDA-DISSERTATION-2015.pdf: 171014393 bytes, checksum: 1cdab0a12f4bb14ef66885168fb9ddbe (MD5) LICENSE.txt: 4211 bytes, checksum: 49d31757203821c488f546005e32f518 (MD5) Previous issue date: 2015-04-17","Embargo set by: Seth Robbins for item 79976 Lift date: 2017-07-22T22:46:21Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 79976 on 2017-07-23T09:15:34Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards a neocortically-inspired ab initio cellular model of associative memory"]}]}],"canonical_facts":{"dc:contributor":["Levinson, Stephen E.","Baryshnikov, Yuliy","Milenkovic, Olgica","Rothganger, Fredrick"],"dc:creator":["Duda, Alexander Michael"],"dc:date":["2015-07-22T22:45:19Z","2017-07-23T09:15:34Z","2015-05","2015-04-17","2015-5"],"dc:description":["We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory inputs that provide access to the richness, complexity, and noise of real-world signals. Specifically, the systems we design and implement are ab initio (simulated) spiking neural networks (SNNs) with cellular resolution and complex network topologies that evolve according to spike-timing dependent plasticity (STDP). We desire to understand how external signals (like speech, vision, etc.) are encoded in the dynamics of such SNNs. In particular, we desire to identify and confirm the extent to which various network-level measurements are information-preserving and could be used as the basis of an associative memory. The dissertation details the relevant background and results of a series of experiments designed to accomplish this objective. The results provide encouraging empirical evidence that such a model can be used for encoding attractors with multi-sensory inputs and across sensory modalities, which both emphasize the potential of such a model for use as a multi-modal associative memory.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-05-01","The student, Alexander Duda, accepted the attached license on 2015-04-15 at 10:36.","The student, Alexander Duda, submitted this Dissertation for approval on 2015-04-15 at 11:18.","This Dissertation was approved for publication on 2015-04-17 at 11:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7863 on 2015-07-22 at 14:24:27","Made available in DSpace on 2015-07-22T22:45:19Z (GMT). No. of bitstreams: 2 DUDA-DISSERTATION-2015.pdf: 171014393 bytes, checksum: 1cdab0a12f4bb14ef66885168fb9ddbe (MD5) LICENSE.txt: 4211 bytes, checksum: 49d31757203821c488f546005e32f518 (MD5) Previous issue date: 2015-04-17","Embargo set by: Seth Robbins for item 79976 Lift date: 2017-07-22T22:46:21Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 79976 on 2017-07-23T09:15:34Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/78735"],"dc:language":["en"],"dc:rights":["Copyright 2015 Alexander Duda"],"dc:subject":["Ab Initio Cellular Models","Associative Memory","Attractors","Complex Networks","Emergence","Information-Preserving","Multi-Scale Modeling","Neurorobotics","Nonlinear Dynamics","Real-World Coupling","Spiking Neural Networks","Spike-timing Dependent Plasticity","Spike-timing Dependent Plasticity (STDP) Learning","Topological Adaptation"],"dc:title":["Towards a neocortically-inspired ab initio cellular model of associative memory"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:12Z"}