About the Conference

The International Conference on Neural Coding and Artificial Intelligence Systems (ICNCAIS) provides a premier interdisciplinary forum for researchers and practitioners across neuroscience, neural coding and representations, machine learning, deep learning, brain-inspired artificial intelligence, theoretical computer science (TCS), and network science. The conference aims to advance a principled understanding of how information is encoded, represented, processed, learned, and retrieved in biological and artificial neural systems, and how these principles of neural coding and representation can be systematically translated into the design of AI systems that leverage neural representations, including brain-inspired machine learning and deep learning models, algorithms, and architectures, alongside mathematically rigorous and brain-inspired research in theoretical computer science.

ICNCAIS places neural coding and representations and AI systems that use neural representations at the core of its scope. In this view, neural representations serve as a unifying interface between biological brain coding and artificial intelligence systems. The conference welcomes contributions on biological and artificial neural coding, population and latent-space representations, encoding and decoding mechanisms, representational geometry, and comparative analyses of biological and artificial neural networks. It further encourages research on AI systems built around neural representations, including embedding-based retrieval systems, retrieval-augmented generation (RAG), vector databases, approximate nearest neighbor search, and other representation-centric architectures. Strong emphasis is placed on theoretical, statistical, and algorithmic foundations, as well as network science perspectives, including connectomics, neural and cognitive networks, network dynamics, and brain-inspired graph and network algorithms, bridging neuroscience, AI, TCS, and network science to enable robust, interpretable, and scalable intelligent systems.