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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 5
FORCE training with fast learning rates reduces decoder correlation and swappability.
A–C Row balanced networks of 2000 neurons were train on a supervisors over a 
grid of points in the (Q,G) parameter space for both LIF and LIF-matched rate networks. The learning rates used were: 
, 
, and 
for the pitchfork, Ode to Joy, and oscillator respectively. Each set of heatmaps from top to bottom are: the L2 testing error for the LIF networks, the L2 testing error for the rate networks, and the Pearson correlation between the learned decoders of the spiking and rate networks. The stars indicate the most correlated pair of networks with both networks L2 error below a threshold of 
, which were used in remaining panels. D–F Sample overlaid network outputs (black), sample neuron firing rates (blue), and target supervisor (grey) for both the spiking (solid) and rate (dotted) networks. G–I Sample output and neuron dynamics for swapped decoders. The top plots are the output and neuron dynamics for the LIF network with the trained firing rate deocder. The bottom plots are the output and neuron dynamics for the firing rate network with the trained LIF deocder. J–L Scatter plot of LIF decoder 
versus firing rate decoder 
with a linear fit.
doi: https://doi.org/10.1371/journal.pcbi.1013224.g005