5 citations · 10 across the 11 of their papers we have counts for
11 papers
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…
Cortico-cerebellar modularity as an architectural inductive bias for efficient temporal learning
Alexandra Voce, Emmanouil Giannakakis, Claudia Clopath
The cerebellum and cerebral cortex form tightly coupled circuits thought to support flexible and efficient temporal processing. How this interaction shapes cortical learning dynami…
Diffusion of Neuromodulators for Temporal Credit Assignment
João Barretto-Bittar, Anna Levina, Emmanouil Giannakakis +1
Biological learning achieves temporal credit assignment despite sparse and imprecise feedback, often relying on neuromodulatory signals acting over space and time. Here, we introdu…
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…
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…
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…
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…