activity
20212026
most citedWhen to be critical? Performance and evolvability in different regimes of neural Ising agents

5 citations · 12 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2026

Architecture Generalization with MetaNCA

Meet Barot, Daniel Berenberg, Sina Khajehabdollahi

Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological neurons, through local interac…

cs.NE2026

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…

cs.AI2025

Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata

Sina Khajehabdollahi, Gautier Hamon, Marko Cvjetko +3

Discovering diverse visual patterns in continuous cellular automata (CA) is challenging due to the vastness and redundancy of high-dimensional behavioral spaces. Traditional explor…

cs.NE2024

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…

q-bio.NC2024

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…

cs.NE2023★ 1 cited

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…

cs.NE2023

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…

q-bio.NC2023

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…

cs.NE2023★ 5 cited

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…

cs.CV2021★ 2 cited

Assessing aesthetics of generated abstract images using correlation structure

Sina Khajehabdollahi, Georg Martius, Anna Levina

Can we generate abstract aesthetic images without bias from natural or human selected image corpi? Are aesthetic images singled out in their correlation functions? In this paper we…

cs.NE2021★ 4 cited

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…