5 citations · 10 across the 11 of their papers we have counts for
6 papers · 1 filter
Distilling a Modular Reservoir Through a Genomic Bottleneck
Mani Hamidi, Sina Khajehabdollahi, Charley M. Wu +1
The intricate structures of biological neural networks largely emerge during development, guided by a comparatively compressed blueprint encoded in the genome. The connectivity tha…
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…
Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks
Sina Khajehabdollahi, Roxana Zeraati, Emmanouil Giannakakis +3
Recurrent neural networks (RNNs) in the brain and in silico excel at solving tasks with intricate temporal dependencies. Long timescales required for solving such tasks can arise f…
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…
When to be critical? Performance and evolvability in different regimes of neural Ising agents
Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis +2
It has long been hypothesized that operating close to the critical state is beneficial for natural, artificial and their evolutionary systems. We put this hypothesis to test in a s…
The dynamical regime and its importance for evolvability, task performance and generalization
Jan Prosi, Sina Khajehabdollahi, Emmanouil Giannakakis +2
It has long been hypothesized that operating close to the critical state is beneficial for natural and artificial systems. We test this hypothesis by evolving foraging agents contr…