Computational geometry for modeling neural populations: From visualization to simulation
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-1891
- Type
- D - Journal article
- DOI
-
10.1371/journal.pcbi.1006729
- Title of journal
- PLoS Computational Biology
- Article number
- e1006729
- First page
- -
- Volume
- 15
- Issue
- 3
- ISSN
- 1553-734X
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
C - BMH (Applied Computing in Biology, Medicine and Health)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The method presented in this paper allows the simulation of large-scale neuronal networks. It retains the biological realism of spiking neuron simulations, but uses at least an order of magnitude less memory, whilst remaining efficient. Simulations that previously required an HPC cluster can now be run on a single PC equipped with a GPGPU. Its implementation in simulator MIIND is now planned for EBRAINS (Human Brain Project Simulation Platform). Invitation to co-author “The Scientific Case for Brain Simulations” (https://www.cell.com/neuron/fulltext/S0896-6273(19)30290-9) published in leading journal NEURON came on the back of the simulation method and implementation discussed in this paper.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
