%0 Journal Article
%A Nestler, Sandra
%A Helias, Moritz
%A Gilson, Matthieu
%T Statistical temporal pattern extraction by neuronal architecture
%J Physical review research
%V 5
%N 3
%@ 2643-1564
%C College Park, MD
%I APS
%M FZJ-2023-03196
%P 033177
%D 2023
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:001074650500002
%R 10.1103/PhysRevResearch.5.033177
%U https://juser.fz-juelich.de/record/1010690