5 papers
Finite integration time can shift optimal sensitivity away from criticality
Sahel Azizpour, Viola Priesemann, Johannes Zierenberg +1
Sensitivity to small changes in the environment is crucial for many real-world tasks, enabling living and artificial systems to make correct behavioral decisions. It has been shown…
Modular Growth of Hierarchical Networks: Efficient, General, and Robust Curriculum Learning
Mani Hamidi, Sina Khajehabdollahi, Emmanouil Giannakakis +3
Structural modularity is a pervasive feature of biological neural networks, which have been linked to several functional and computational advantages. Yet, the use of modular archi…
Learning with 3D rotations, a hitchhiker's guide to SO(3)
A. René Geist, Jonas Frey, Mikel Zhobro +2
Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. Th…
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent
Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural co…
Revising clustering and small-worldness in brain networks
Tanguy Fardet, Emmanouil Giannakakis, Lukas Paulun +1
As more connectome data become available, the question of how to best analyse the structure of biological neural networks becomes increasingly pertinent. In brain networks, knowing…