Biologically Inspired Neural Path Finding
Li H, Khan Q, Tresp V, Cremers D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13406 LNAI
Pages Range: 329-342
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783031150364
DOI: 10.1007/978-3-031-15037-1_27
Abstract
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths, in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code accompanying this work can be found here: https://github.com/hangligit/pathfinding.
Involved external institutions
Germany (DE)How to cite
APA:
Li, H., Khan, Q., Tresp, V., & Cremers, D. (2022). Biologically Inspired Neural Path Finding. In Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 329-342). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Li, Hang, et al. "Biologically Inspired Neural Path Finding." Proceedings of the 15th International Conference on Brain Informatics, BI 2022, Virtual, Online Ed. Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong, Springer Science and Business Media Deutschland GmbH, 2022. 329-342.
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