Modeling oscillatory phase and phase synchronization with neuronal excitation and input strength in cortical network
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 057-188147-14958
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2018.2845301
- Title of journal
- Ieee Access
- Article number
- -
- First page
- 36441
- Volume
- 6
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
http://bura.brunel.ac.uk/handle/2438/17414
- 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
-
3
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work provided strong evidence to prove that oscillatory phase and phase synchronization in cortical networks have quantifiable relationships with neuronal excitation and input strength. A unique contribution of this study is its construction of a computational model by following spectral computation and physiological experimental procedures. The innovative computational model can be applied to a wide range of real-world applications including robotics, wheelchair, video games, virtual car. This interdisciplinary study, which integrates cognitive science and computer science, has led to further investigations in this area and resulted in more publications, e.g., IEEE Access (2020) (https://doi.org/10.1109/ACCESS.2020.2978161)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
