Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates
Fig 2
FORCE training spiking LIF and LIF-matched rate networks.
The single layer recurrent neural network used in the FORCE method consists of: a set of fixed reservoir weights, a set of fixed encoder weights and a set of learned decoder weights. The reservoir network creates a chaotic pool of rich mixed dynamics which are used to linearly decode the target supervisor with
. This decoder
(S for spikes, R for rates) is learned online using the Recursive Least Squares (RLS) algorithm. The encoder weights
then feedback the decoded output
into the reservoir to stabilize the dynamics.