I want to experiment with an alternative BCM rule where the learning-rate is negative and the thresholding factor selects for neurons that are firing less than the average rate. However, I’d rather not have to go through the whole Nengo build system to accomplish this. @tcstewar mentioned doing this with a
nengo.Node first to allow for quick iteration. However, I’m unclear how to do this properly? Are there any examples of this in any repositories?
The best I can currently come up with is to have a
nengo.Node that takes in both the
post ensemble’s spikes, while outputting a modification of the
pre filtered spikes given the saved state of the learning rule. Is this how everyone else has done it?