5 papers
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…
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…
Locally adaptive cellular automata for goal-oriented self-organization
Sina Khajehabdollahi, Emmanouil Giannakakis, Victor Buendia +2
The essential ingredient for studying the phenomena of emergence is the ability to generate and manipulate emergent systems that span large scales. Cellular automata are the model…
Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem. We hypothesize that the e…