The brain-AI convergence: Predictive and generative world models for general-purpose computation

• URL: http://arxiv.org/abs/2512.02419v1
• Date: Tue, 02 Dec 2025 05:03:14 GMT
• Title: The brain-AI convergence: Predictive and generative world models for general-purpose computation
• Authors: Shogo Ohmae, Keiko Ohmae,
• Abstract summary: Recent advances in AI systems with attention-based transformers offer a potential window into how the neocortex and cerebellum give rise to diverse functions.<n>We identify shared computational mechanisms in the attention-based neocortex and the non-attentional cerebellum.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in general-purpose AI systems with attention-based transformers offer a potential window into how the neocortex and cerebellum, despite their relatively uniform circuit architectures, give rise to diverse functions and, ultimately, to human intelligence. This Perspective provides a cross-domain comparison between the brain and AI that goes beyond the traditional focus on visual processing, adopting the emerging perspecive of world-model-based computation. Here, we identify shared computational mechanisms in the attention-based neocortex and the non-attentional cerebellum: both predict future world events from past inputs and construct internal world models through prediction-error learning. These predictive world models are repurposed for seemingly distinct functions--understanding in sensory processing and generation in motor processing-- enabling the brain to achieve multi-domain capabilities and human-like adaptive intelligence. Notably, attention-based AI has independently converged on a similar learning paradigm and world-model-based computation. We conclude that these shared mechanisms in both biological and artificial systems constitute a core computational foundation for realizing diverse functions including high-level intelligence, despite their relatively uniform circuit structures. Our theoretical insights bridge neuroscience and AI, advancing our understanding of the computational essence of intelligence.

Related papers

• Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence [55.07411490538404]
  We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM)<n>IFM aims to acquire the underlying mechanisms of intelligence by learning directly from diverse intelligent behaviors.
  arXiv  Detail & Related papers  (2025-11-13T09:28:41Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [132.77459963706437]
  This book provides a comprehensive overview, framing intelligent agents within modular, brain-inspired architectures.<n>It explores self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities.<n>It also examines the collective intelligence emerging from agent interactions, cooperation, and societal structures.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• Shifting Attention to You: Personalized Brain-Inspired AI Models [3.0128071072792366]
  We show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model over doubles behavioral performance compared to the unmodified CLIP baseline.<n>Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human-computer interaction.
  arXiv  Detail & Related papers  (2025-02-07T04:55:31Z)
• The brain versus AI: World-model-based versatile circuit computation underlying diverse functions in the neocortex and cerebellum [0.0]
  We identify similarities and convergent evolution in the brain and AI.<n>We propose a new theory that integrates established neuroscience theories.<n>Our systematic approach, insights, and theory promise groundbreaking advances in understanding the brain.
  arXiv  Detail & Related papers  (2024-11-25T04:05:43Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Brain-inspired Computational Intelligence via Predictive Coding [73.42407863671565]
  Predictive coding (PC) has shown promising properties that make it potentially valuable for the machine learning community.<n>PC-like algorithms are starting to be present in multiple sub-fields of machine learning and AI at large.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• A brain basis of dynamical intelligence for AI and computational neuroscience [0.0]
  More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
  arXiv  Detail & Related papers  (2021-05-15T19:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.