{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/123625"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/123625","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"Estimation of Shared Functional Information Between Neural Areas","abstract":"Recent technological advances now allow for detailed recordings of brain activity, capturing thousands of neurons over several days in animals engaged in complex behaviors. These datasets provide a unique opportunity to study how information is shared across neural areas during visually guided behaviors. In this thesis we quantify both the extent and the content of this shared information in the mouse brain during visual processing. Recent studies have suggested that neural areas share information in a distributed manner, meaning that similar information is simultaneously transmitted to multiple regions but processed differently within each. To investigate this idea, we utilize publicly available large-scale recordings of neural activity from mice performing visually guided decision-making tasks provided by the Allen Institute. These datasets include multi-unit activity recorded with Neuropixels probes across several brain regions, including visual cortex, thalamus, and hippocampus, enabling a detailed examination of distributed coding. To explore the distributed coding idea, we employ Reduced Rank Regression (RRR) to define a novel connectivity metric based on how well the activity of a target region can be predicted from a low-dimensional subspace of a source region. This framework allows us to characterize distributed signals through directed inter-areal connections. Using this approach, we compare connectivity across behavioral states (active versus passive) and stimulus conditions (change versus no-change). Our results demonstrate that neural populations encode stimulus features in a structured and distributed manner, and that the information shared between regions depends on task engagement and trial type. Moreover, the geometry of inter-areal subspaces, quantified using geodesic distances, reveals distinct communication patterns across brain states and cortical hierarchies. Our findings support the view that neural processing is fundamentally distributed, with shared population-level codes spanning multiple regions. Our methodology provides a novel approach for studying inter-areal communication and offers new insights into how the brain dynamically routes information in response to task demands.","abstract_html":"Recent technological advances now allow for detailed recordings of brain activity, capturing thousands of neurons over several days in animals engaged in complex behaviors. These datasets provide a unique opportunity to study how information is shared across neural areas during visually guided behaviors. In this thesis we quantify both the extent and the content of this shared information in the mouse brain during visual processing. Recent studies have suggested that neural areas share information in a distributed manner, meaning that similar information is simultaneously transmitted to multiple regions but processed differently within each. To investigate this idea, we utilize publicly available large-scale recordings of neural activity from mice performing visually guided decision-making tasks provided by the Allen Institute. These datasets include multi-unit activity recorded with Neuropixels probes across several brain regions, including visual cortex, thalamus, and hippocampus, enabling a detailed examination of distributed coding. To explore the distributed coding idea, we employ Reduced Rank Regression (RRR) to define a novel connectivity metric based on how well the activity of a target region can be predicted from a low-dimensional subspace of a source region. This framework allows us to characterize distributed signals through directed inter-areal connections. Using this approach, we compare connectivity across behavioral states (active versus passive) and stimulus conditions (change versus no-change). Our results demonstrate that neural populations encode stimulus features in a structured and distributed manner, and that the information shared between regions depends on task engagement and trial type. Moreover, the geometry of inter-areal subspaces, quantified using geodesic distances, reveals distinct communication patterns across brain states and cortical hierarchies. Our findings support the view that neural processing is fundamentally distributed, with shared population-level codes spanning multiple regions. Our methodology provides a novel approach for studying inter-areal communication and offers new insights into how the brain dynamically routes information in response to task demands.","abstract_has_math":false,"creators":["Karimi, Farzad"],"institution":"Graduate Studies","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Physics & Astronomy","degree_department":null,"school":null,"contributors":[],"advisors":["Orlandi, Javier"],"committee_chairs":[],"committee_members":["Cone, Jackson","Nicola, Wilten","Towlson, Emma"],"year":2025,"date_issued":"2025-12-19","date_published":"2025-12-19","updated_at":"2026-07-24T01:30:18Z","subjects":["Reduced Rank Regression","Dimensionality Reduction","Neuropixel Recording","Distributed Coding","Parallel Processing","Geodesic Distance","Low Dimensional Communication"],"languages":["en"],"rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. 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This framework allows us to characterize distributed signals through directed inter-areal connections. Using this approach, we compare connectivity across behavioral states (active versus passive) and stimulus conditions (change versus no-change). Our results demonstrate that neural populations encode stimulus features in a structured and distributed manner, and that the information shared between regions depends on task engagement and trial type. Moreover, the geometry of inter-areal subspaces, quantified using geodesic distances, reveals distinct communication patterns across brain states and cortical hierarchies. Our findings support the view that neural processing is fundamentally distributed, with shared population-level codes spanning multiple regions. 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