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Biologically Inspired Neural Path Finding

MCML Authors

Link to Profile Volker Tresp

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

Link to Profile Daniel Cremers PI Matchmaking

Daniel Cremers

Prof. Dr.

Director

Computer Vision & Artificial Intelligence

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.

inproceedings

BI 2022

15th International Conference on Brain Informatics. Padova, Italy, Jul 15-15, 2022.

Authors

H. Li • Q. Khan • V. Tresp • D. Cremers

Links

DOI GitHub

Research Areas

 A3 | Computational Models

 B1 | Computer Vision

BibTeXKey: LKT+22

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