[nengo-user] Adjusting topological connections

Mon Aug 8 13:56:07 EDT 2016

yes , that's exactly what I wanted to do. Using node would solve my problem.
The only remaining point is now the input to the node is some firing rate
(x) , I want for example b.neurons[0] to have an output depending on input
from a.neurons[0] ONLY.

On Mon, Aug 8, 2016 at 7:45 PM, Terry Stewart <terry.stewart at gmail.com>
wrote:

> Hmm, I'm still not quite sure what you're trying to do here.  What do
> you mean by a "function for the rate of spikes"?  Do you mean neuron
> model itself (i.e. the thing that takes input current and generates
> spikes)?  If so, that's already part of the Ensemble and is specified
> by the neuron_type parameter, and you can choose from LIF, LIFRate,
> Izhikevich, Sigmoid, or write your own.  Or do you want to do
> something to the spikes after they've been generated by the neurons
> and before they're fed to the next group of neurons?  What sort of
> thing are you envisioning here?
>
> That said, in general, you can implement anything you want in Nengo by
> creating a Node:
>
> -------------------
> import nengo
>
> model = nengo.Network()
> with model:
>     a = nengo.Ensemble(n_neurons=50, dimensions=1)
>     b = nengo.Ensemble(n_neurons=50, dimensions=1)
>
>     def func(t, x):
>         # do whatever you want in here, where x is the input spikes and
>         # you return the output activity
>         return x
>     mynode = nengo.Node(func, size_in=50, size_out=50)
>
>     nengo.Connection(a.neurons, mynode)
>     nengo.Connection(mynode, b.neurons)
> ----------------------
>
>
> I'm still not sure whether that's what you're looking for, however....
>
>
> Terry
>
> On Mon, Aug 8, 2016 at 1:34 PM, Omar Zahra <omar.zahra at ejust.edu.eg>
> wrote:
> > This part I couldn't actually understand from the documentation well. I
> > thought that It wasn't about getting "the optimal connection weights" , I
> > just wanted to apply some function for the rate of spikes, and the output
> > from the function would be input tp the postsynaptic.
> > Sorry for that misunderstanding , how then would I apply some function to
> > the rate of spike ?
> > Thanks for your patience
> >
> > On Mon, Aug 8, 2016 at 6:04 PM, Terry Stewart <terry.stewart at gmail.com>
> > wrote:
> >>
> >> I think the problem is that you're trying to specify a function as
> >> well.  What function are you trying to do?
> >>
> >> The problem is that you're specifying the connection weights in two
> >> different ways.  When you save function=something, Nengo uses that
> >> function to find the optimal connection weights to approximate that
> >> function.  So you can't also manually specify the connection weights,
> >> since that's exactly what you've told nengo to solve for on its own!
> >>
> >> Terry
> >>
> >> On Mon, Aug 8, 2016 at 12:00 PM, Omar Zahra <omar.zahra at ejust.edu.eg>
> >> wrote:
> >> > Hello Terry,
> >> >
> >> > Thanks for your reply. I already used this to make a connection.
> >> > My problem is that I cannot " nengo.Connection(a.neurons, b.neurons,
> >> > transform=matrix, function = func)" because it must be applied to an
> >> > Ensemble.
> >> > I hope you have some solution for this problem.
> >> >
> >> > On Mon, Aug 8, 2016 at 5:47 PM, Terry Stewart <
> terry.stewart at gmail.com>
> >> > wrote:
> >> >>
> >> >> Hello Omar,
> >> >>
> >> >> If you want to use Nengo to do manual neuron-to-neuron connection
> >> >> (i.e. the sort of thing that would happen in a standard neural
> >> >> simulator), then you need to connection to the ens.neurons object.
> >> >> For example, here's a quick way to do random connections between two
> >> >> groups of neurons:
> >> >>
> >> >> --------------
> >> >> import nengo
> >> >> import numpy as np
> >> >>
> >> >> model = nengo.Network()
> >> >> with model:
> >> >>     a = nengo.Ensemble(n_neurons=50, dimensions=1)
> >> >>     b = nengo.Ensemble(n_neurons=50, dimensions=1)
> >> >>
> >> >>     matrix = np.random.normal(size=(50, 50))
> >> >>     nengo.Connection(a.neurons, b.neurons, transform=matrix)
> >> >> -------------------------
> >> >>
> >> >> Let us know if that helps for your situations!
> >> >>
> >> >> Also, for future questions, we've just started up an online forum at
> >> >> https://forum.nengo.ai/
> >> >>
> >> >> Terry
> >> >>
> >> >> On Mon, Aug 8, 2016 at 9:13 AM, Omar Zahra <omar.zahra at ejust.edu.eg>
> >> >> wrote:
> >> >> > Hello,
> >> >> >
> >> >> > I am new to NENGO and also just started using python to deal with
> >> >> > NENGO.
> >> >> > I
> >> >> > would like to build part of the brain by connecting some layers.
> >> >> > These
> >> >> > connections are supposed to be topological. I would like also to
> >> >> > apply
> >> >> > some
> >> >> > function across these connections. When I try to use connection to
> >> >> > the
> >> >> > whole
> >> >> > ensemble, I cannot define the connections perfectly as done in case
> >> >> > of
> >> >> > making connections neuron by neuron -using Ensemble.neurons[] -. I
> >> >> > tried
> >> >> > even increasing the dimensions of the ensemble and setting the
> >> >> > encoders
> >> >> > such
> >> >> > as to give seperate action for each neuron, still some unintended
> >> >> > response
> >> >> > appears.
> >> >> > Reply ASAP please. Thanks in advance
> >> >> >
> >> >> > --
> >> >> > Best Regards
> >> >> > Omar Ibn ElKhatab AbdAllah Zahra
> >> >> >
> >> >> > _______________________________________________
> >> >> > nengo-user mailing list
> >> >> > nengo-user at ctnsrv.uwaterloo.ca
> >> >> > http://ctnsrv.uwaterloo.ca/mailman/listinfo/nengo-user
> >> >> >
> >> >
> >> >
> >> >
> >> >
> >> > --
> >> > Best Regards
> >> > Omar Ibn ElKhatab AbdAllah Zahra
> >
> >
> >
> >
> > --
> > Best Regards
> > Omar Ibn ElKhatab AbdAllah Zahra
>

-- 
Best Regards
Omar Ibn ElKhatab AbdAllah Zahra
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