q-bio.NCApr 19, 2026

NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

Authors: Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski, Gina Adam, James B Aimone, Akwasi Akwaboah, Yiannis Aloimonos, Carmen Amo Alonso, +23 more

Organizations: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. · Institute for Neural Computation, UC San Diego, La Jolla, CA, USA. · Mathematics Department, Kalaheo High School, Kailua, HI, USA. · Dept. Neurobiology, UC San Diego, La Jolla, CA, USA. · Dept. Electrical & Computer Engineering, George Washington University, Washington, DC, USA. · Neural Exploration & Research Laboratory, Sandia National Laboratories, Albuquerque, NM, USA. · Dept. Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. · Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA. · Dept. Aeronautics & Astronautics, Stanford University, Stanford, CA, USA. · Event-Driven Perception for Robotics, Italian Institute of Technology, Genoa, Italy. · Dept. Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. · Dept. Neuroscience, Princeton University, Princeton, NJ, USA. · Dept. Biology, University of Washington, Seattle, WA, USA. · Depts. Biology, Neurosciences & Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. · Inst. of Neuroinformatics, UZH-ETH Zurich, Zurich, Switzerland. · Dept. Control & Dynamical Systems, Caltech, Pasadena, CA, USA. · Dept. Electrical & Computer Engineering, UC Santa Cruz, Santa Cruz, CA, USA. · Dept. Electrical & Computer Engineering, University of Cincinnati, Cincinnati, OH, USA. · Dept. Engineering, Oakland University, Rochester, MI, USA. · Dept. Computer Science, Rochester Institute of Technology, Rochester, NY, USA. · Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal. · School of Computer Science + Montreal Neurological Institute, McGill University & Mila, The Quebec AI Institute. · Institute of Computational Life Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland. · School of Electrical Engineering & Computer Science, Penn State University, University Park, PA, USA. · Dept. Physiology, UC San Francisco, San Francisco, CA, USA. · Dept. Computer Science and Biomedical Engineering, Yale University, New Haven, CT, USA.

Abstract

Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.

Explore similar work

CardsList
Jul 15, 2026q-bio.NC

Grounded world models in biological organisms and future embodied AI

Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems remain largely based on passive training regimes where linguistic regularities create the scaffold onto which information from other modalities is attached. Conversely, neuroscience and cognitive science suggest that biological intelligence is organized in the opposite way, where grounded world models acquired through interaction with the environment provide the semantic scaffold to which language is attached. Here, we illustrate five examples of neural circuits supporting grounded world modelling, which underlie navigation in physical and conceptual spaces, affordance-based perception and interaction with objects, active perception and exploratory learning, allostatic control and emotion, and the distinction between self- and world-generated outcomes. These examples highlight several features largely missing from current embodied AI, including the role of intrinsic dynamics as a foundation for learning, the centrality of action in aligning these dynamics with the external world, the prominence of autonomous experience and open-ended learning over passive assimilation of externally provided data, and the fact that early predictive and control mechanisms scaffold higher cognitive abilities such as reasoning, conceptual navigation, planning, imagination, understanding others' minds, and communication. Finally, we discuss whether and how principles derived from biological systems may inform future embodied AI, including training regimes based on social interaction to construct world models that are not only grounded but also socially shared and aligned with human norms and values.
Giovanni Pezzulo, Davide Nuzzi, Marco D'Alessandro +3
Embodied Artificial IntelligenceWorld ModelsCognitive ScienceBiological
AskPDF
Date pendingcs.CL

NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis

AI is rapidly advancing neuroscience, yet many laboratories fail to fully unleash its potential due to significant interdisciplinary barriers. While pre-trained neural models for physiological data are progressing quickly, their heterogeneous architectures and modality-specific constraints hinder systematic integration, selection, and evaluation. Despite recent advances in large language model (LLM)-based agent systems for intelligent scientific applications, existing approaches often still lack the domain expertise required to effectively select and coordinate diverse neuroscience pre-trained models and handle unique data types in this domain. We present NS-Copilot, an LLM-driven multi-agent system for neuroscience analysis that autonomously supports end-to-end workflows for diverse professional tasks. It unifies domain-specific pre-trained models and supports key neuroscience modalities, including EEG and extracellular spike data, through a natural-language interface. Given raw data and a task description, NS-Copilot orchestrates agents with specialized roles for planning, adaptive control, code generation, and result synthesis, enabling analysis without dataset-specific heuristics. We evaluate NS-Copilot on neuroscience benchmarks spanning Alzheimer's disease, Parkinson's disease, and working memory spike decoding. Across 8 trials per task, the system consistently outperforms strong baselines on the primary metric, demonstrating the ability of NS-Copilot for effective and scalable neuroscience analysis. Our code is publicly available at https://github.com/FrankLiu1102/ns-copilot.
Wuche Liu, Yiran Qiao, Linlin Hou +4
NeuroscienceCopilotBinary SpikesDomain Adaptation
AskPDF
Aug 4, 2026cs.AI

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning

Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural networks and language models with symbolic reasoning algorithms to function in high-stakes domains or in low-data regimes that characterize many real-world applications. We argue that the neurosymbolic combination of machine learning and formal reasoning is not a niche approach within AI, but rather includes many already successful techniques that are of crucial importance to the development of reliable, efficient and, ultimately, trustworthy systems. This perspective prompts a re-examination of the design of current AI systems. We show that many leading AI systems, including some that are not traditionally considered as neurosymbolic, can be analysed from the perspective of four principles of neurosymbolic AI design: Reasoning, Assurances, Interfacing and Learning (RAIL). Applying the RAIL framework offers a unified view of seemingly disparate AI systems, ranging from physics-aware machine learning to neuro-guided search (such as Google DeepMind's Alpha-* suite), causal learning and tool-augmented Large Language Models. Importantly, the RAIL principles will enable engineers to make better-informed and more principled decisions about the design and deployment of production-level AI systems. In this article, we introduce the RAIL principles, examine how they can be applied across major areas of AI, and illustrate how they may guide practitioners to integrate neurosymbolic methods into next-generation AI technologies.
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Artificial Intelligence SystemsPrinciples
AskPDF