Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum
- Submitting institution
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Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185749054
- Type
- D - Journal article
- DOI
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10.3389/fncom.2017.00119
- Title of journal
- Frontiers in Computational Neuroscience
- Article number
- 119
- First page
- -
- Volume
- 11
- Issue
- -
- ISSN
- 1662-5188
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- URL
-
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- Supplementary information
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- 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
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4
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This innovative work was funded by the ERC project Self-Constructing Computing Systems
(216593) with the aim of demonstrating that dynamic learning of the classical Hebbian paradigm
is applicable to the modern emerging learning approach STDP. The paper is highly innovating by
demonstrating that dynamic local behaviour results in computationally independent synapses.
This is uniquely achieved using deterministic time independent modelling of the synaptic state,
and has steadily been increasing recognition. The information in this paper is the basis for a new funding proposal to Brain Research on dynamic network stability during neural growth.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -