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We examine the differences manifested by the inclusion and exclusion of biologically motivated interareal laminar connections on the computational roles of different neuronal populations in the microcircuit of hierarchically related areas throughout learning. Our findings show that the presence of feedback connections correlates with the functional modularization of cortical populations in different layers and provides the microcircuit with a natural inductive bias to differentiate expected and unexpected inputs at initialization, which we justify mathematically. Furthermore, when testing the effects of training the microcircuit and its variants with a predictive-coding-inspired strategy, we find that doing so helps better encode noisy stimuli in areas of the cortex that receive feedback, all of which combine to suggest evidence for a predictive-coding mechanism serving as an intrinsic operative logic in the cortex.<\/jats:p>","DOI":"10.1162\/neco.a.23","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T15:05:16Z","timestamp":1753369516000},"page":"1551-1599","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the Architectural Biases of the Cortical Microcircuit"],"prefix":"10.1162","volume":"37","author":[{"given":"Aishwarya","family":"Balwani","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA 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