Direct connections (with BCM rule) between individual neurons

[FilipD]

Hi @xchoo

Wow! Thank you very much for all the knowledge you’ve posted and for the research you’ve made! Each of your posts is a part of the whole solution!
Code examples are very helpful also, because, without the knowledge about the structure of the Nengo, it’s much harder to write new functions or classes.

@xchoo

My idea is to create what is essentially a copy of the BCM rule, with one critical change.

Your proposed solution is great and definitely I’m gonna try it!

I still wonder about performance a little bit - is it possible to make it better - if somehow I could define continous ranges of indices between neurons (let’s say 1 neuron to 40 subsequent neurons) and have as much connections as neurons in upper layer (say 400), then I will have less to calculate (400 vectors of 40 elements = 16 000 synapses). But If make a big connection matrix (say 400 x 400 = 160 000 synapses), I’ll have a lot of zero-weigth synapses to calculate - only 1/10th of calculated will be useful. Am I right?

---

@xchoo

Unlike something like tensorflow, Nengo doesn’t have a concept of “physical” arrangements of neurons within an ensemble. That being said, the advantage of that is that one ensemble can be physically interpreted as being any physical dimensionality (even a 3D arrangement if you want). But, the downside is that the physical arrangement of the neurons is up to the user to determine, by using the appropriate transformation matrices between connections.

Yeah, I see. The Nengo focuses on the NEF approach, where we don’t have any 3D structure, rather functional structure, achieved by the whole ensembles. It’s good to hear, that my plans for using Nengo are ok.

---

@xchoo

Numpy has various functions that can do this sort of mapping for you. As an example, Numpy’s ravel_multi_index function converts a tuple of indices, and a given dimensionality of the matrix into the equivalent single-dimensional (flat) index values.

Great, thanks for the developer and you! Instead of Numpy, I wanted to write my own functions to calculate indices, but the Numpy solution is better, at least for the performance.

---

@xchoo

In Nengo, custom learning rules can be “register” with the Nengo builder (that builds the Nengo simulator object), but for NengoOCL, you need to create a subclass of the NengoOCL simulator with calls to your custom learning rule inside it.

Ok, I’m gonna dive into it. First I have to try with Python Nengo, then with OCL.

Again, thank you very much, for such a big help!