Back to Search
Start Over
Simulating structural plasticity of the brain more scalable than expected.
- Source :
-
Journal of Parallel & Distributed Computing . Jan2023, Vol. 171, p24-27. 4p. - Publication Year :
- 2023
-
Abstract
- Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes–Hut algorithm. They demonstrate good scalability and prove a runtime complexity of O (n log 2 n). In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as O (n log n). • Improved serial runtime bound for the adapted Barnes–Hut algorithm to Θ(n*log(n)). • Improved the parallel runtime bound to Θ(n/p*log(n)+p). • Mathematical justification that the given runtime bound is sharp. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SYNAPSES
*SCALABILITY
*ALGORITHMS
*NEURONS
Subjects
Details
- Language :
- English
- ISSN :
- 07437315
- Volume :
- 171
- Database :
- Academic Search Index
- Journal :
- Journal of Parallel & Distributed Computing
- Publication Type :
- Academic Journal
- Accession number :
- 159797369
- Full Text :
- https://doi.org/10.1016/j.jpdc.2022.09.001