NeuroAI and Beyond

• URL: http://arxiv.org/abs/2601.19955v1
• Date: Tue, 27 Jan 2026 01:57:51 GMT
• Title: NeuroAI and Beyond
• Authors: Jean-Marc Fellous, Gert Cauwenberghs, Cornelia Fermüller, Yulia Sandamisrkaya, Terrence Sejnowski,
• Abstract summary: Neuroscience and Artificial Intelligence (AI) have made significant progress in the past few years but have only been loosely inter-connected.<n>We identify current and future areas of synergism between these two fields.<n>We advocate for the development of NeuroAI, a type of Neuroscience-informed Artificial Intelligence.
• Score: 10.165763848540522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuroscience and Artificial Intelligence (AI) have made significant progress in the past few years but have only been loosely inter-connected. Based on a workshop held in August 2025, we identify current and future areas of synergism between these two fields. We focus on the subareas of embodiment, language and communication, robotics, learning in humans and machines and Neuromorphic engineering to take stock of the progress made so far, and possible promising new future avenues. Overall, we advocate for the development of NeuroAI, a type of Neuroscience-informed Artificial Intelligence that, we argue, has the potential for significantly improving the scope and efficiency of AI algorithms while simultaneously changing the way we understand biological neural computations. We include personal statements from several leading researchers on their diverse views of NeuroAI. Two Strength-Weakness-Opportunities-Threat (SWOT) analyses by researchers and trainees are appended that describe the benefits and risks offered by NeuroAI.

Related papers

• 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)
• Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning [42.086960710257564]
  We explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances.<n>We introduce the concepts of dropin'' for neurogenesis and revisit dropout'' and structural pruning for neuroapoptosis.<n>We conclude by advocating for greater research efforts in this interdisciplinary domain.
  arXiv  Detail & Related papers  (2025-03-27T12:09:04Z)
• A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives [2.5782420501870296]
  Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise.<n>Deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability.<n>Neuro-symbolic AI (NeSy) attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations.
  arXiv  Detail & Related papers  (2025-03-23T21:33:38Z)
• Augmenting learning in neuro-embodied systems through neurobiological first principles [42.810158068175646]
  We describe recent bioinspired models, learning rules, and architectures for augmenting artificial neural networks.<n>We propose a framework for augmenting ANNs, which has the potential to bridge the gap between neuroscience and AI.<n>We show how integrating biophysical principles into task-driven spiking neural networks and neuromorphic systems provides scalable solutions.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI [33.0761784111292]
  Neuro-symbolic AI emerges as a promising paradigm to enhance interpretability, robustness, and trustworthiness. Recent NSAI systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities.
  arXiv  Detail & Related papers  (2024-01-02T05:00:54Z)
• 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)
• A Rubric for Human-like Agents and NeuroAI [2.749726993052939]
  Contributed research ranges widely from mimicking behaviour to testing machine learning methods. It cannot be assumed nor expected that progress on one of these three goals will automatically translate to progress in others. This is clarified using examples of weak and strong neuroAI and human-like agents.
  arXiv  Detail & Related papers  (2022-12-08T16:59:40Z)
• Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
  Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years. NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
  arXiv  Detail & Related papers  (2022-10-28T04:38:10Z)
• Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution [102.45290975132406]
  Neuroscience has long been an important driver of progress in artificial intelligence (AI) We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI.
  arXiv  Detail & Related papers  (2022-10-15T17:18:37Z)
• Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems [120.297940190903]
  Recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
  arXiv  Detail & Related papers  (2022-05-02T18:00:10Z)
• Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs [6.385006149689549]
  We will review the emerging opportunities of on-chip AI for the next-generation implantable brain-machine interfaces (BMIs) We will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.
  arXiv  Detail & Related papers  (2022-04-04T12:47:07Z)

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.