Organized by Friedemann Zenke, Tim Vogels and Guillaume Hennequin – Full schedule

Computation in the brain occurs through complex interactions in highly structured, non-random networks. Moving beyond traditional approaches based on statistical physics, engineering-based approaches are bringing new vistas on circuit computation, by providing novel ways of

• building artificial yet fully functional model circuits,
• dissecting their dynamics to identify new circuit mechanisms, and
• reasoning about population recordings made in diverse brain areas across a range of sensory, motor, and cognitive tasks. Thus, the same “science of real-world problems” that is behind the accumulation of increasingly rich neural datasets is now also being recognized as a vast and useful set of tools for their analysis.

This workshop aims at bringing together researchers who build and study structured network models, spiking or otherwise, that serve specific functions. Our speakers will present their neuroscientific work at the confluence of machine learning, optimization, control theory, dynamical systems, and other engineering fields, to help us understand these recent developments, critically evaluate their scope and limitations, and discuss their use for elucidating the neural basis of intelligent behaviour.