Title: Neural Networks for Complex Systems: From Epidemic Modeling to Swarm Robotics
Abstract: Deep learning models, inspired by the structure of the brain, were first developed in the last century. These models are trained to recognize patterns in large amounts of data. Recently, deep learning has made a big impact, both in research and in everyday applications, like healthcare, image recognition, and language translation.
However, despite their advancements, these models still fall short of the abilities found in biological brains, which are adaptable, energy-efficient, and have evolved over millions of years. In contrast, artificial models are specialized and struggle to adapt to new information.
To help address this gap, we have developed a robotic experiment that combines the programmability of artificial neural networks with some of the physical constraints seen in biological systems.
Thesis: https://hdl.handle.net/2077/84676
Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Hamid Kellay
Committee: Maria Guix Noguera, Juliane Simmchen, Michael Felsberg
Alternate board member: Paolo Vinai