Brain-inspired computing looks to mimic how the human brain works to improve artificial intelligence (AI) systems. This area has gained a lot of interest recently because it helps us create stronger and more efficient AI models while tackling challenges faced by current artificial neural networks.

This workshop will cover a range of topics, including biological neural networks, cognitive computing, and biologically-inspired algorithms. We will discuss how learning from the brain's structure and operations can lead to new solutions for complex issues in AI, machine learning, and data processing.

The workshop will include talks from experts in the field and interactive panel discussions. Participants will have the chance to collaborate, share ideas, and connect with others who are excited about using biological principles to advance technology.

Full program in this link.

Schedule
9:00 AM - 9:30 AM Speaker: Rui Ponte Costa, University of Oxford
A theory of self-supervised learning in cortical layers
9:30 AM - 10:00 AM Speaker: Guillaume Bellec, Vienna University of Technology
Validating biological mechanisms in deep brain models with optogenetic perturbation testing
10:00 AM - 10:30 AM Speaker: Guozhang Chen, Peking University
Characteristic differences between computationally relevant features of cortical microcircuits and artificial neural networks
10:30 AM - 11:00 AM Coffee Break
11:00 AM - 11:30 AM Speaker: Robert Legenstein, Graz University of Technology
Rapid learning with phase-change memory-based neuromorphic hardware through learning-to-learn
11:30 AM - 12:00 PM Speaker: Shogo Ohmae, Chinese Institute for Brain Researc
World-model-based versatile computations in the neocortex and the cerebellum
12:00 PM - 12:30 PM Speaker: Yuliang Zang, Tianjin University
Biological strategies for efficient learning in cerebellum-like circuits
12:30 End of Workshop and Lunch Break

Full program in this link.