Which type of learning rule is used in eth_mnist example?

[rafaelblevin821]

I understand it's based on the Diehl and Cook model, but I cannot see anywhere in the code where it specifies either STDP or PosPre? Does the learning rule automatically come built into the Diehl and Cook imported model?

[SimonInParis]

Yes, the Diehl and Cook model is defined in models.py, and the learning rule rule is PostPre, just as you thought: # Connections w = 0.3 * torch.rand(self.n_inpt, self.n_neurons) input_exc_conn = Connection( source=input_layer, target=exc_layer, w=w, update_rule=PostPre, nu=nu, reduction=reduction, wmin=wmin, wmax=wmax, norm=norm, )

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On Mon, Jun 20, 2022 at 2:25 PM davidh_tsp ***@***.***> wrote: I understand it's based on the Diehl and Cook model, but I cannot see anywhere in the code where it specifies either STDP or PosPre? Does the learning rule automatically come built into the Diehl and Cook imported model? — Reply to this email directly, view it on GitHub <#556>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKZ2IOHURQL3WHHIBN6TJS3VQBPM5ANCNFSM5ZIYBPWQ> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[rafaelblevin821]

Hi, thanks for clarifying. I still cannot find this in the documentation though.

[Hananel-Hazan]

Thanks @SimonInParis
Here is the reference @rafaelblevin821
documentation and the lines Simon pointing out

[rafaelblevin821]

Thank you both for the information.