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| 001 | 1010690 | ||
| 005 | 20240313103123.0 | ||
| 024 | 7 | _ | |a 10.1103/PhysRevResearch.5.033177 |2 doi |
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| 100 | 1 | _ | |a Nestler, Sandra |0 P:(DE-Juel1)174585 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Statistical temporal pattern extraction by neuronal architecture |
| 260 | _ | _ | |a College Park, MD |c 2023 |b APS |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 1 |u fzj |
| 700 | 1 | _ | |a Gilson, Matthieu |0 P:(DE-Juel1)184621 |b 2 |
| 773 | _ | _ | |a 10.1103/PhysRevResearch.5.033177 |g Vol. 5, no. 3, p. 033177 |0 PERI:(DE-600)3004165-X |n 3 |p 033177 |t Physical review research |v 5 |y 2023 |x 2643-1564 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1010690/files/INV_23_AUG_011700.pdf |
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