2026 CapoCaccia Neuromorphic Workshop

  Hierarchies, claustrum, circuits, and bee dances The day opened with Henry Kennedy on a humbling premise: we still do not really understand the cortex . At the center of the problem is hierarchy. Cortical hierarchy is often described as a progression: information moves “up” through increasingly complex representations, building larger receptive fields and integrating more context. The classical visual system - from David Hubel and Torsten Wiesel’s receptive field work in primary visual cortex onward - remains the textbook example. But Henry’s point was that the real anatomy makes this picture much messier. There are far more feedforward than feedback pathways, but both are everywhere. Information does not simply travel “up” and then “down” - much of it unfolds simultaneously, with recurrent and reverse hierarchies constantly interacting. This naturally led into predictive coding. In that framework, feedforward signals carry prediction errors, while feedback signals carry predicti...

  The morning discussion began with a question: how much of intelligence is already encoded in the genome? Tony Zador from CSHL arguedthat neural networks cannot simply “do anything.” If we want artificial systems to become more intelligent, we need to understand what makes biological brains special.  Tony’s early interest focused on increasing computational power within neurons themselves - for example through dendritic computations. Later, this shifted toward the need to understand neural circuits in much greater detail, motivating the development of large-scale connectivity mapping tools such as BARseq. A central theme of the talk was a critique of the heavy focus on learning in modern machine learning. Tony argued that learning is often overrated, at least as a model for how organisms operate in the real world. This brought us back to the old nature-versus-nurture debate.  Drawing on Moravec’s paradox, Tony argued that animals come equipped with billions of years...

  Today felt like a shift in perspective. The discussions kept circling a deeper question: What is the (biological and artificial) substrate of computation and how much does it matter? Three speakers approached this from different angles: Tobi Delbruck → sparsity and physical limits Melika Payvand → richer neuron models and input-dependent dynamics Walter Senn → dendrites, gain modulation, and links to attention/transformers The morning discussions moved across levels: from hardware constraints (sparsity), to neural (gated) dynamics, to cognitive function (attention in dendrites), and back again to... how do we design intelligent systems? Sparsity dominates everything (?) Tobi started from a deceptively simple question: What is the actual operating regime of the brain? He sparked discussion on brain insights and shared numbers/observations to understand efficiency.   ~10¹⁵ synapses Avg ~1–10 Hz firing rates (very low) ~10⁻¹⁴ J per synaptic event (energy per synaptic operati...