Organized by: Giancarlo La Camera, Luca Mazzucato, Jin Wang and Michael R. Douglas
This workshop brings together leading researchers to discuss current progress on uncovering emergent phenomena and design principles of brain function. The workshop is organized to encourage discussion across different research communities including physics, mathematics, machine learning, and neuroscience with a special emphasis on dynamic aspects of neural activity. Neurons are capable of a wide repertoire of collective dynamical behaviors, and converging evidence suggests that these collective dynamics play a fundamental role in cognitive function. However, the fundamental principles governing neural network dynamics, as well as the mechanisms of neural dynamics supporting complex computation, are largely unknown. Understanding the brain will ultimately depend on our ability to infer the neural code from the statistical analysis of complex high dimensional data, and to explain it in terms of physical models based on neural networks. Progress in this direction has been accelerated by the fruitful contamination of neuroscience with statistical mechanics, dynamical systems, machine learning, information theory and other fields. This workshop will be a unique opportunity to exchange and integrate leading views on very large datasets, state-of-the-art analysis tools, biologically plausible models and dynamic models of cortical computation.
This workshop is associated with the program: Neural networks and the Data Science Revolution: from theoretical physics to neuroscience, and back: January 6-31, 2020