Publications

• WBAI Wiki
• 2023
  • BRAES: Developing Brain Reference Architecture Editorial System for Accumulating Hypotheses on Neural Functionality
  • Proposal of an Extensive SCID method for reverse engineering a wide range of brain regions
  • BDBRA: Database for extracting descriptions of neural projections and brain regions from neuroscience literature with dialogue prompts
• 2022
  • Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots [arXiv in 2021]
  • Hippocampal formation-inspired probabilistic generative model [arXiv in 2021]
  • Brain-inspired probabilistic generative model for double articulation analysis of spoken language
  • Poster: The great crossword: Potential for rapid progress in understanding the function of the entire brain
•  2021
  • Hippocampal formation-inspired probabilistic generative model
  • Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots
  • The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain
•  2020
  • Revealing the computational meaning of neocortical interarea signals
  • Planning with Brain-inspired AI
  • Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation
  • Attentional Reinforcement Learning in the Brain
• 2019
  • Consciousness Boundary: Claustrum defines the imaginary area of the cortex
  • Consciousness for realize general intelligence in the brain – Modal information is needed for episode –
  • Which parts of the brain circuit are responsible for attentional mechanisms in machine learning? poster at SNL-2019
  • Peacekeeping Conditions for an Artificial Intelligence Society
  • Socially Beneficial Agents with Biologically Inspired Artificial Intelligence, a presentation @ the AI to Bridge Borders Conference
  • Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
  • FAVAE: Sequence Disentanglement using Information Bottleneck Principle  
• 2018
  • Whole brain connectomic architecture to develop general artificial intelligence
  • Construction of a functional hypothesis about local brain circuit by integrating neuroscientific knowledge as a framework. Poster at AINI2018
  • Connectome informatics: reverse engineering of whole brain networks. Poster at AINI2018
  • Function Map-Driven Development for AGI (BICA 2018)
  • Hierarchical Variational Recurrent Autoencoder with Top-Down prediction (poster) (Joint symposium of 10th Optogenetics Research Conference and Second International Symposium on Brain Information Dynamics 2018)
  • WBA Symposium at the 28th Annual Conference of the Japanese Neural Network (JNNS2018; The proceedings are available here.)
    • Strategy to Build Beneficial General-Purpose Intelligence Inspired by Brain (Slides)
    • BriCA Kernel: Cognitive Computing Platform for Large-scale Distributed Memory Environments
    • Development of Biologically Inspired Artificial General Intelligence Navigated by Circuits Associated with Tasks
    • Do top-down predictions of time series lead to sparse disentanglement?
    • Visualization of Morphism Tuples of Equivalence Structures
•  2017
  • Whole brain connectomic architecture to develop general artificial intelligence (BICA2017)
  • Reinterpreting The Cortical Circuit (paper | slides) (AGA 2017: IJCAI Workshop on Architectures for Generality & Autonomy)
•  2016
  • Whole Brain Architecture Approach Is a Feasible Way Toward an Artificial General Intelligence, in Proc. of ICONIP 2016
  • The Whole Brain Architecture Initiative, in Proc. of ICONIP 2016
    The full text:  This is a post-peer-review, pre-copyedit version of an article published in ICONIP 2016: Neural Information Processing pp. 316-323. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-46687-3_35.
  • Realizing Linguistic Functions via the Whole Brain Architecture Approach
•  2015
  • A Generic Software Platform for Brain-Inspired Cognitive Computing (BICA2015)