2013/04/05 by Yuriy V. Pershin, Massimiliano Di Ventra
Computer Science · Engineering · Mathematics · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Artificial intelligence #Combinatorics #Computer network #Computer science #K shortest path routing #Mathematical optimization #Mathematics #Neuroscience and Neural Engineering #Path (computing) #Photoreceptor and optogenetics research #Self-organization #Shortest path problem #Theoretical computer science #Topology (electrical circuits) #cond-mat.dis-nn #cs.ET #physics.comp-ph
paper · pdf · doi:10.1103/physreve.88.013305
published as Phys. Rev. E 88, 013305 (2013) · arXiv admin note: substantial text overlap with arXiv:1211.4487
arxiv created 2013/04/05 · openalex publication_date 2013/07/09 · arxiv updated 2016/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We show that memristive networks, namely networks of resistors with memory, can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time nonlocality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.