[nengo-user] Adjusting topological connections

[Terry Stewart]

You should be able to do that with the node, since the return value
from that node is the input to b. x[0] is the output of a.neurons[0],
and so if your return value's [0] element is just based on x[0], then
the input to b.neurons[0] will be based only on the output from
a.neurons[0].

Terry

On Mon, Aug 8, 2016 at 1:56 PM, Omar Zahra <omar.zahra at ejust.edu.eg> wrote:
> 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