ICNCE 2026

Abstract: While artificial neural networks represent a highly successful mapping from neuroscience AI, and clearly capture important aspects neuronal information processing, data from experimental neuroscience strongly suggests that the ANN abstraction omits important biological computational principles. This includes such features as the pulsed nature of neural communication, the diversity and function of neuronal morphology, and the inherently time-continuous mode of operation. To investigate these computational principles, we need to be able to train large and complex networks of spiking neurons for specific tasks. I will show how effective online and approximate learning rules enable the supervised training of large-scale networks of detailed spiking neuronal models, how we can integrate extended temporal delays in a principled and efficient manner, and how these spiking neural network models can be integrated with brain-derived decision-making circuits to operate continuously. As I will argue, this approach opens up the investigation of both network and neuronal architectures based on functional principles, while at the same time demonstrating the potential power and energy efficiency of AI-solutions based on neuromorphic computing as embodied by spiking neural networks.

Bio: Prof. Dr. Sander M. Bohté heads the CWI Machine Learning group, and is also a part-time full professor of Cognitive Computational Neuroscience at the University of Amsterdam, The Netherlands. He received his PhD in 2003 at CWI on the topic of “Spiking Neural Networks” and then spent a Post-doc wat the University of Colorado in Boulder. In 2004, he rejoined CWI as scientific staff. In 2016, he co-founded the CWI Machine Learning group, where his research bridges the field of neuroscience and deep learning, addressing topics like local learning, online learning and learning in deep spiking neural networks.