Population Density Equations for Stochastic Processes with Memory Kernels
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-1890
- Type
- D - Journal article
- DOI
-
10.1103/PhysRevE.95.062125
- Title of journal
- Physical Review E
- Article number
- 062125
- First page
- -
- Volume
- 95
- Issue
- 6
- ISSN
- 1539-3755
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- 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
-
1
- Research group(s)
-
C - BMH (Applied Computing in Biology, Medicine and Health)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- With the advent of multi-electrode arrays covering large groups of individual neurons, population level modelling becomes vital in understanding brain function. This paper removes the need for the standard assumption in computational neuroscience that spikes trains are Poisson distributed; experimental data suggest that non-Markovian processes should be used. Some earlier work addressed this but is the first method that allows using any point model neuron (1D or 2D) combined with gamma distributed spike processes as a model for non-Markovian spike trains. The technique will be deployed on EBRAINS (Human Brain Project simulation platform) as part of the MIIND simulator.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
