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Solution to XOR problem using spiking neural networks. Currtently working on a liquid state machine and training with ReSuMe rule.
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XoR problem works. I've included the weights, delays, desired times, and so forth, as the solution.
e9c8371View on GitHubThere is a bug in the SetNumSpikes routines I think. Some neurons are not spiking afterwards.
578f10dView on GitHubWait, SetNumberSpikes function seems to be going in circles!
43c80dbView on GitHubReSuMe seems to work for one input. Now to re-write it and add the output printout to show how well training was done.
a61a055View on GitHubFixed Issues with network storing / restoring aspects. Now, setting no. spikes works, it seems.
7146107View on GitHubJust found some issues with the network storing / restoring aspects.
13db6e7View on GitHubNow it can compute the info needed for setting the desired times.
40f6394View on GitHubWorking on the routine that finds the spike-time range. Will convert it to binary-like search.
db9fd64View on GitHubSETING NO. SPIKES WORKS! Along with reading/writing files! WooHoo!
5b0dee4View on GitHubMerge branch 'brian2_lists_net' of github.com:syntapy/brian_xor into brian2_lists_net
64cdb46View on GitHubOk, reading / writing weights / delays seems to work now.
accc929View on GitHubOk, reading / writing weights / delays seems to work now.
3e8df36View on GitHubTesting version: first merge with initial.py didn't work!
37b97d1View on GitHubModifications to the weight reading and weight writing functions in initial.py, plus adding in saving / reading delays.
7c75187View on GitHub