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001010690 1001_ $$0P:(DE-Juel1)174585$$aNestler, Sandra$$b0$$eCorresponding author$$ufzj
001010690 245__ $$aStatistical temporal pattern extraction by neuronal architecture
001010690 260__ $$aCollege Park, MD$$bAPS$$c2023
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001010690 520__ $$aNeuronal 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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001010690 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b1$$ufzj
001010690 7001_ $$0P:(DE-Juel1)184621$$aGilson, Matthieu$$b2
001010690 773__ $$0PERI:(DE-600)3004165-X$$a10.1103/PhysRevResearch.5.033177$$gVol. 5, no. 3, p. 033177$$n3$$p033177$$tPhysical review research$$v5$$x2643-1564$$y2023
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