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Brain connectivity meets reservoir computing

Fig 3

Memory capacity task.

(A) (Upper) Schematic representation of the task. An input signal (X) is feed as a time series into the network through an input neuron. Each output neuron independently learns a lagged version of the input (Y_τ) (Lower) Alternative representation of the task in terms of the input/output structure of the data. (B) Examples of network evaluation on the task. A forgetting curve (grey line) is shown for each tested species (columns) and connectivity W condition (color coded). For each time lag (τ) the score is plotted (squared Pearson correlation coefficient, ρ²). The memory capacity (MC, see legends) is defined as the sum of performances over all values of τ and represents the shaded areas in the plotted examples. (C) Performance of the bio-instantiated echo state networks (BioESNs) for the three different species tested. For each pattern length, 100 different networks with newly instantiated weights were trained (4000 time steps) and tested (1000 time steps). The test performance of each networks is represented by a point in the plots.

doi: https://doi.org/10.1371/journal.pcbi.1010639.g003