| Project/Area Number |
19K20348
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
Li Yuzhe 沖縄科学技術大学院大学, 神経計算ユニット, スタッフサイエンティスト (30815436)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
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| Keywords | Bayesian inference / Decision making / Uncertainty / Cortical layers / Calcium imaging / functional connectivity / dimensionality reduction / metastate / cortical layers / prism lens / calcium imaging / factor analysis / Bayesian PCA / transfer entropy / multiple cortical layers / clustering / somatosensory cortex / active lever / operant conditioning |
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| Outline of Research at the Start |
Bayesian brain theory explains a wide range of sensorimotor behaviors. It hypothesizes that actions are guided by experience and sensory evidence. This project aims to investigate the neural mechanisms of Bayesian-like behaviors on the basis of the multi-layer structure of the cortical cortex.
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| Outline of Final Research Achievements |
This project aims to investigate how animals make decisions in uncertain environments and how neural activity across different cortical layers contributes to the decision-making process. To achieve this, we designed a lever push/pull task for mice and implanted a prism lens in the cortex to simultaneously capture calcium imaging data from multiple layers. To analyze the noisy calcium imaging data, we developed a dual ARD (Automatic Relevance Determination) method for dimensionality reduction, which also was used for decoding and encoding in subsequent analyses. Furthermore, we have explored the temporal and spatial connectivity patterns among neurons from different layers. We are currently utilizing Bayesian models to analyze the neural activities. By integrating the various approaches we have developed throughout this project and continuing our ongoing efforts, we anticipate uncovering valuable insights into decision-making process that takes place among different cortical layers.
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| Academic Significance and Societal Importance of the Research Achievements |
The exploration of neural activity patterns across cortical layers provides insights into how different layers of the brain communicate and organize information. Understanding how decision-making occurs at the neural level will deepen our understanding of human behavior, cognition, and perception.
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