Learning feature relationships in CNN model via relational embedding convolution layer DOI
Shengzhou Xiong, Yihua Tan, Guoyou Wang

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 179, P. 106510 - 106510

Published: July 6, 2024

Language: Английский

Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons DOI Creative Commons

Chris Kang,

Jasmine A. Moore, Samuel Robertson

et al.

Neural Networks, Journal Year: 2025, Volume and Issue: 187, P. 107308 - 107308

Published: March 12, 2025

Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative tools to characterize structures. Network science provides an ideal mathematical framework with which systems of interacting components, has transformed our understanding across domains, including mammalian brain. Yet, little been done bring ANNs. In this work, propose that leverage adapt methods measure both global- local-level characteristics Specifically, focus on structures efficient multilayer perceptrons as a case study, sparse systematically pruned such they share real-world networks. We use adapted metrics show pruning process leads emergence spanning subnetwork (lottery ticket perceptrons) complex architecture. This exhibits global local characteristics, heavy-tailed nodal degree distributions dominant weighted pathways, mirror patterns observed human neuronal connectivity. Furthermore, alterations precede catastrophic decay performance is heavily pruned. science-driven approach analysis artificial serves valuable tool establish improve fidelity, increase interpretability, assess Significance Statement become increasingly complex, often diverging from counterparts To design plausible "brain-like" architectures, whether advance neuroscience research or explainability, it essential these optimally resemble counterparts. offer information about interconnected systems, brain, attracted much attention for analyzing Here, present significance work: •We analyze structural demonstrate organizational similar those brain emerge through alone. convergence features compelling evidence optimality processing capabilities. •Our significant first step towards science-based networks, potential shed light fidelity

Language: Английский

Citations

0
NeuroSimWorm: A multisensory framework for modeling and simulating neural circuits of Caenorhabditis elegans DOI
Jiaxin Wang, Mengxiao Zhang, Kejun Wang

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130055 - 130055

Published: April 1, 2025

Language: Английский

Citations

0
Knowledge-Enhanced Large Language Models for Ideation to Implementation: A New Paradigm in Product Design DOI

Zhinan Li,

Zhenyu Liu, Guodong Sa

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113147 - 113147

Published: April 1, 2025

Language: Английский

Citations

0
A learning orientation detection system and its application to grayscale images DOI
Tianqi Chen, Yuki Todo, Zeyu Zhang

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: unknown, P. 112901 - 112901

Published: Dec. 1, 2024

Language: Английский

Citations

0
Learning feature relationships in CNN model via relational embedding convolution layer DOI
Shengzhou Xiong, Yihua Tan, Guoyou Wang

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 179, P. 106510 - 106510

Published: July 6, 2024

Language: Английский

Citations

0