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I have been reading latley on SNN and decided to try and implement myself some simple simulation. So I wrote a simple MATLAB simulation using simple LIF current based model and I try using STDP to classify unsupervised MNIST data such as here: https://www.frontiersin.org/articles/10.3389/fncom.2015.00099/full#

(So, images converts to poisson spike train by intensity)

I'm using simple window based STDP updates and most of the times I'm getting that a single same neuron in the hidden layer spikes for any input sample that I feed in (i.e. only neuron #50 spikes for any given input).

I would really appreciate if anyone can clarify the following for me:

  1. Is there a way to normalize/regularize the weights updates such that this neuron dominance won't happen?

  2. Any general tips on how to monitor/verify the STDP convergence? max/min of weights? positive STDP update larger than negative STDP update?

Again, would really appreciate any help. Thanks

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  • $\begingroup$ maybe I can help you, but your question is not very clear, so if you can just drop here the code so that I can understand your problem. Best regards! $\endgroup$ – rayan moukhadder Jan 2 at 19:06

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