The 32nd International Conference on Neural Information Processing (ICONIP 2025) is set to be held in Okinawa, Japan, from November 20th to November 24th, 2025. ICONIP serves as the annual flagship conference of the Asia Pacific Neural Network Society (APNNS). ICONIP 2025 also serves as the 35th Annual Meeting of the Japanese Neural Network Society (JNNS).

Over the past three decades, ICONIP has emerged as a premier venue for showcasing cutting-edge research, technology, and innovations in computational modelling, data analytics, and artificial intelligence.

ICONIP 2025 aims to provide a high-level international forum for scientists, researchers, educators, industrial professionals, and students worldwide to present the state of research and development, address new challenges, and discuss trends in neural information processing theory and applications. These include computational neuroscience, machine learning, bioinformatics, health informatics, computer vision, automation and control, finance, manufacturing, transportation, cybersecurity, and more.

In addition to technical sessions with oral and poster presentations, the conference program will include special sessions, workshops, and tutorials on topics of current interest. It also features keynote sessions led by the world’s leading researchers and professionals, as well as awards to outstanding papers presented at the conference.

ICONIP employs a competitive paper reviewing process using OpenReview to ensure that only high-quality papers are accepted for publication. Proceedings will be published in the Springer series of Lecture Notes in Computer Science (LNCS) and Communications in Computer and Information Science (CCIS). All accepted papers will be open access from OpenReview about two weeks before the conference to facilitate in-depth discussions. Please take that into account if you intend to apply for patents, etc.

Topics of the conference

ICONIP2025 invites high quality contributions from, but not limited to the topics

    Theory and Algorithms
        Machine learning
        Explainable AI
        Neural network models
        Neurodynamics
        Responsible AI
    Computational Neurosciences
        Models of learning and cognition
        Neural data analysis
        Brain-machine interface
        Computational psychiatry
    Applications and Frontiers
        Big data analysis
        Generative AI
        Natural language processing 
        Robotics and control
        Healthcare
        Information security
        Neuromorphic hardware
        Privacy and security for AI