Topic: cumulants

Neuronal systems need to process temporal signals. Here, we hypothesize that temporal (co-)fluctuations – corresponding to high-order statistics beyond the average activity – are relevant for computation. The proposed biologically inspired feedforward neuronal model is able to extract information from up to the third order cumulant to perform time series classification. This model relies on a nonlinear gain function to combine the different statistical orders of the inputs, after a usual weighted summation. In addition to the afferent synaptic weights, the nonlinear gain function is optimized,