Hi Xavier,
If you know the time course of you poisson process rates beforehand, you can use the
inhomogeneous_poisson_generator, which changes to new rates at given time points.
The rate calculation of the poisson generator is precisely on of the cases where
SetStatus
inside a RM context causes problems. This will not change, because one of the key points
about the RM context is to avoid time overhead for neuron configuration.
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
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From: Xavier Otazu <xotazu(a)cvc.uab.cat>
Date: Friday, 9 February 2024 at 11:44
To:
<users(a)nest-simulator.org>
Subject: [NEST Users] Re: Weights assignment error with mpirun
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]
Hi Hans,
Inside an RM context, I am not only changing synaptic weights, but also Poisson spike
generators rates. At every loop iteration I change the "input image", and I
simulate it using a spike generator. I tried an RM context some time ago (previous NEST
versions) and I realized that rate didn't change (I guess because the non-updated
internal variables). Is this behaviour changed on 3.6 version?
Related to the neuroscientific question I understand you ask me about the temporal
evolution of weights when externally modifying them. When I just used STDP, my weights
grow indefinitely (a well known problem) or just saturates to W_max. Since in many
models
input weights are normalized, I applied normalization to avoid this growing problem (in
addition, there are some biophyisical studies showing that this kind of normalization
exists in biological systems). Another question is the "continuity" (first
derivative) of temporal evolution of weights ... the answer is "I do not know"
:-/
Best,
Xavier
Hans Ekkehard Plesser wrote:
Hi Xavier,
Actually, in your case the Prepare-Run-Cleanup approach should work, since you use
SetStatus only on synaptic weights. The reason we warn about unpredictable behaviour in
case of SetStatus inside a RunManager context is that Prepare() calls a pre_run_hook()
on
all network nodes to allow them to precompute internal variables. So if you changed
neuron
parameters inside an RM context, the nodes would update with an inconsistent set of
externally set parameter values and internally not-updated variables. Synapses do not
have
such a pre-run-hook, so there you should be way safe. Note that you cannot
Connect/Disconnect in a RM context, since the connection infrastructure needs to be
updated by Prepare().
Finally, one could wonder about how the external manipulation of the synaptic weights
interacts with the internal evolution of the weights through STDP, i.e., what the
actual
time-evolution equations for your weights become when taking normalization into
account.
But that is a neuroscientific question, not a NEST-technical one.
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home
http://arken.nmbu.no/~plesser
From: Xavier Otazu <xotazu(a)cvc.uab.cat>
Date: Thursday, 8 February 2024 at 21:42
To:
users(a)nest-simulator.org <users(a)nest-simulator.org>
Subject: [NEST Users] Re: Weights assignment error with mpirun
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Hi Hans,
Great news to be able to call GetConnections() just once ... it speedsA LOT my runnings
(from 230-ish seconds to 90-ish) :-)
I tried the Prepare/Run/Cleanup some time ago (nice solution), but since I have to use
SetStatus() to update normalized weights between Prepare() and Cleanup(), I cannot use
it
:-(
Best,
Xavier
Hans Ekkehard Plesser wrote:
Hi Xavier,
The result of `GetConnections()` remains valid as long as no new synapses are added or
synapses deleted, so in your case, you only need to call it once.
When you then do a `conn.weight`, you will get the weights at that point in time. But
note
that NEST only updates synaptic weights when a spike actually passes through the
synapse,
so you will get for each synapse the weight as it was at the end of the plasticity
update
when the most recent spike passed through that synapse.
Instead of calling Simulate() repeatedly, you can also use a RunManager context for
higher
efficiency, see
https://nest-simulator.readthedocs.io/en/stable/nest_behavior/running_simul….
In case you should be wondering why global instantaneous normalization is so
cumbersome
in
NEST: We have focused on support for biologically plausible networks so far and
instantaneous global normalization is just not biologically plausible (nor physically
feasible with a view to causality).
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home
http://arken.nmbu.no/~plesser
From: Xavier Otazu <xotazu(a)cvc.uab.cat>
Date: Thursday, 8 February 2024 at 19:46
To:
users(a)nest-simulator.org <users(a)nest-simulator.org>
Subject: [NEST Users] Re: Weights assignment error with mpirun
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Hi Hans,
Thanks for your information. Fortunately, I do not normalize output connections (only
input), hence, this code works for me.
Another question. Take that I call nest.Simulate() inside a for loop and I want to
normalize weights after every iteration. In order to avoid calling GetConnections()
for
every loop iteration, may I get the connections calling GetConnections() just once?
May
I
safely suppose that after every Simulate() new weights of my connections (they are
changed
because I use STDP) will be available (for reading) using the same variable? An
example
code would be something like:
my_conn = GetConnections(...)
for iter in range(...)
nest.Simulate(...)
normalize_connections(my_conn) # function that normalizes weights and calls
nest.SetStatus(my_conn, ...)
Many thanks in advance!
Xavier
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