Neural architectures in action: Linking structure to computation across scales

With increasingly detailed data on neural circuitry structures becoming available, the computational significance of these intricate architectures has become central to neuroscience research. This workshop explores computational models and theories across multiple scales—from dendritic computation in single neurons to microcircuits and brain-scale networks—that leverage these architectural insights to develop novel computational paradigms, address longstanding questions, and propose new explanations of brain function. Speakers will highlight how structural motifs may represent fundamental computational primitives for both theoretical understanding and practical applications. Special emphasis will be placed on how structural plasticity mechanisms adjust and adapt these structures to enable computation and learning beyond traditional synaptic weight adjustments, on the use of dendritic compartmentalization in learning and computation, and on network motifs in lateral and large-scale networks. We bring together researchers across neuroscience disciplines to advance our understanding of how neural architecture shapes computation in biological, artificial, and neuromorphic systems.