ICNCE 2026

Abstract: Biological intelligence is remarkably data- and energy-efficient. We strive to understand and replicate this in artificial intelligence. A key step toward this goal is to develop robust, scalable learning algorithms for physical and biologically inspired neural networks.Achieving this requires rethinking fundamental principles of representation learning and credit assignment. In...

Abstract: Brain networks exhibit a prominent hierarchical organization at all levels, from anatomical organization to activity patterns. In contrast, artificial deep neural networks are organized sequentially using the end-to-end error backpropagation algorithm for training. This algorithm propagates information forward and backward through the entire network, updating its parameters in...

Abstract: This talk explores our success and failures with neuromorphic language models and their deployment on Intel Loihi 2. We have achieved the first billion parameter LLMs running on neuromorphic hardware at 2-watts, moving state-of-the-art reasoning from the datacenter to the edge. We have spent a painful amount of time working out when neuromorphic LLMs do and don't make sense, and...

Abstract: The explosive growth of transformer-based AI models and the push toward adaptive intelligence at the edge have exposed fundamental limits of conventional von Neumann hardware, where data movement—not computation—dominates energy and latency. This talk presents recent progress from our group on memory-centric co-design spanning devices, circuits, and architectures to address these...