Kayson Fakhar

Across 25,000 generative-model networks scored on 29 properties, 635 human and 224 mammalian connectomes cluster in one narrow niche, trading wiring economy for computational capability and robustness.

This paper benchmarks 16 brain network similarity measures, finding DeltaCon is most accurate for parameter recovery but no measure excels across all criteria.

The paper presents a game-theoretic model showing human cortical networks prioritize computational reliability over communication efficiency and wiring cost.

The paper shows that curiosity and brain-like constraints can generate complex neural architectures and cognitive abilities in artificial networks.

This paper introduces Network Reciprocity Control algorithms to adjust asymmetry and reciprocity in networks while preserving density and weight, demonstrating their impact on network properties and scalability.

This study examines age-related changes in brain network connectivity during motor and cognitive tasks, finding domain-specific adaptations in older adults.

The paper shows that connectivity asymmetry in brain networks supports higher working-memory capacity, while increased reciprocity reduces it, using computational models of recurrent neural networks.

This study introduces a framework using game theory to characterize optimal communication in brain networks, finding that broadcasting models best describe communication efficiency, with rich-club regions playing a key role.

The paper demonstrates that neural activity patterns may not reliably indicate causal contributions of neural units to behavior and introduces a lesioning framework to better infer causal relationships.

The paper systematically lesions an artificial neural network to assess causal contributions of its elements, finding that single-element perturbations are biased while multi-site lesions reveal more accurate functional interactions.