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Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates

Fig 6

Fast learning improves performance of LIF-match rate but not spiking LIF networks.

A Networks of 2000 neurons were train on the Ode to Joy, Fourier basis, and sinusoidal tasks over a grid of points in the (Q,G) hyperparameter space with 4 different learning rates for both spiking and rate networks. Within each sub-panel, in order from left to right, we plotted the testing error for the spiking network, the rate network, and the cross network decoder correlation . B For the 5 Hz sinusoidal oscillator task and (Q,G) hyperparameter point (20,0.125), we trained 21 repetitions of randomly initialized networks with sizes in the range for 4 different learning rates for both the spiking (B.I) and rate model (B.II). Each blue point represents a repetition and each black point the mean. The blue lines indicate the linear regression fit with slope and intercepts indicated. C Mean Pearson correlation coefficient of decoders across networks for simulations in B. The shaded area indicates the corrected sampled standard deviation.

doi: https://doi.org/10.1371/journal.pcbi.1013224.g006