Nengo, WTA and Homeostasis

[Timodz]

Thank you @xchoo for your clear explanation!

Lateral Inhibition

@xchoo

However, I believe that you have made a slight error when defining the inhibitory weights. You used np.full(...) to generate the weights, which creates a lateral inhibitory weights, as well as recurrent inhibitory weights. In effect, the neurons are inhibiting themselves, and I would be surprised if the neurons even fired at all.

Yes indeed, I fixed it now.

Homeostatis

@xchoo

This question really depends on what you want to achieve with “homeostatis”. If you want to achieve a sort of normalization of neural activity across the whole ensemble, then using a homeostatic learning rule might be the way to go (I believe the Oja rule does homeostatis, but I’ll have to double check on that). But, if you just want neural activity that falls to a baseline value after some period of spiking, then the ALIF neuron is an appropriate use.

Yes, its a sort of normalization to prevent a neuron from dominating every time and inhibit the others. I will check the Oja rule.

Event-based Simulations

@xchoo

I’m not 100% certain on this (I’ll message the devs and get back to you shortly), but I don’t believe there is an event-based mode in Nengo (nor does it support event-based processing).

Yes, that could change everything since I am trying to implement an event-based STDP which explains also some of the behaviour i am seeing, and if this is true I would have to go with another type of STDP suitable with Nengo. Waiting for your confirmation.

After taking everything into consideration, the behaviour was always the same and in fact I had also introduced a pause between each input using a modified version of

nengo.processes.PresentInput which I didn’t mention in the previous post, when I removed that pause everything went as expected. I can see the inhibition working now.

Thank you again for the explanation and I hope to hear from you soon about the event-driven possibility.