Workshop Overview

The inaugural Neuro for AI & AI for Neuro: Towards Multi-Modal Natural Intelligence workshop was successfully convened on January 27, 2026, in Singapore. This gathering brought together researchers in artificial intelligence, computational neuroscience, and neuromorphic engineering to advance multimodal natural intelligence through a two-way exchange between biology and machine learning.

On the “Neuro → AI” front, the workshop explored how principles of cortical microcircuitry—such as inhibitory-excitatory balance, sparse coding, dendritic nonlinearities, and structured connectivity—can inspire efficient and robust multimodal architectures. Sessions highlighted how biologically grounded features derived from large-scale cell-type and connectivity datasets can inform transformer and graph-based models, enhancing their learning performance and energy efficiency.

Conversely, the “AI → Neuro” theme showcased how advanced machine-learning techniques are driving new insights into brain function and dysfunction. Presentations and demos featured AI-driven analysis of calcium imaging, electrophysiological, and behavioral data, as well as gradient-based training of biophysically realistic models. Topics included how unsupervised representation learning and causal-inference frameworks can reveal latent circuit motifs, guide closed-loop experiments, and accelerate discovery in both basic and translational neuroscience.

By uniting neuroscience insights with scalable AI tools, the workshop bridged theory, experiment, and application—fostering cross-pollination where AI researchers gained biologically grounded inductive biases, and neuroscientists leveraged modern computational frameworks to advance discovery.

Research Focus Areas