Data for Brain Reference Architecture of NY24CanonicalCorticalMicrocircuitsDecisionMaking
Canonical cortical microcircuits reference architecture for decision making
DOI:
https://doi.org/10.51094/jxiv.1048Keywords:
Brain Reference Architecture, Canonical Cortical Microcircuits, Reinforcement learningAbstract
Canonical cortical microcircuits (CCMs) is the six-layer structure preserved throughout the mammalian neocortex and is thought as a fundamental computational unit. This dataset reverse-engineers CCMs and presents a computational model to achieve decision-making. The data consist of the anatomical connectivity of CCMs and the functions hierarchically achieved from each uniform circuit. First, information on the anatomical connections of CCMs was collected from seven review papers. Next, reinforcement learning was determined as the algorithm for decision-making, which CCMs can implement. Finally, we describe how top-level functions are achieved from excitatory neural populations and circuit motif based on inhibitory neural populations, assigning output semantics to each excitatory neural population. The data are described in a brain reference architecture format and stored in the BRA data repository. This dataset provides experimentally testable hypotheses about neural activity patterns in cortical layers.
Conflicts of Interest Disclosure
Yoshimasa Tawatsuji and Hiroshi Yamakawa are BRAES managers but did not participate in the editorial process or decisions related to this manuscript.Downloads *Displays the aggregated results up to the previous day.
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Submitted: 2025-01-18 12:27:56 UTC
Published: 2025-01-24 01:06:30 UTC
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Copyright (c) 2025
Naohiro Yamauchi
Yoshimasa Tawatsuji
Yudai Suzuki
Kenji Doya
Hiroshi Yamakawa
This work is licensed under a Creative Commons Attribution 4.0 International License.
