This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2023-03196 in citations.
Please use the identifier: http://dx.doi.org/10.1103/PhysRevResearch.5.033177 in citations.
Statistical temporal pattern extraction by neuronal architecture
Statistical temporal pattern extraction by neuronal architecture
Neuronal systems need to process temporal signals. Here, we show how higher-order temporal (co)fluctuationscan be employed to represent and process information. Concretely, we demonstrate that a simple biologicallyinspired feedforward neuronal model can extract information from up to the third-order...
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Personal Name(s): | Nestler, Sandra (Corresponding author) |
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Helias, Moritz / Gilson, Matthieu | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | Physical review research, 5 (2023) 3, S. 033177 |
Imprint: |
College Park, MD
APS
2023
|
DOI: |
10.34734/FZJ-2023-03196 |
DOI: |
10.1103/PhysRevResearch.5.033177 |
Document Type: |
Journal Article |
Research Program: |
Advanced Computing Architectures Human Brain Project Specific Grant Agreement 3 Emerging NC Architectures Computational Principles Neuroscientific Foundations Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) Towards an integrated data science of complex natural systems Transparent Deep Learning with Renormalized Flows |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1103/PhysRevResearch.5.033177 in citations.
Neuronal systems need to process temporal signals. Here, we show how higher-order temporal (co)fluctuationscan be employed to represent and process information. Concretely, we demonstrate that a simple biologicallyinspired feedforward neuronal model can extract information from up to the third-order cumulant to performtime series classification. This model relies on a weighted linear summation of synaptic inputs followed bya nonlinear gain function. Training both the synaptic weights and the nonlinear gain function exposes how thenonlinearity allows for the transfer of higher-order correlations to the mean, which in turn enables the synergisticuse of information encoded in multiple cumulants to maximize the classification accuracy. The approach isdemonstrated both on synthetic and real-world datasets of multivariate time series. Moreover, we show thatthe biologically inspired architecture makes better use of the number of trainable parameters than a classicalmachine-learning scheme. Our findings emphasize the benefit of biological neuronal architectures, paired withdedicated learning algorithms, for the processing of information embedded in higher-order statistical cumulantsof temporal (co)fluctuations. |