Workshop Overview

This workshop will bring 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, we will explore 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 will highlight 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 will showcase how advanced machine-learning techniques are driving new insights into brain function and dysfunction. Presentations and demos will feature AI-driven analysis of calcium imaging, electrophysiological, and behavioral data, as well as gradient-based training of biophysically realistic models. Topics will include 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 aims to bridge theory, experiment, and application—fostering cross-pollination where AI researchers gain biologically grounded inductive biases, and neuroscientists leverage modern computational frameworks to advance discovery.

Research Focus Areas