Barna Thank you for your reply. I think you have identified the core of
the issue. The target weight that I need to use is 470 to achieve a node
weight in the order of one, which seems wrong. I will attempt to explain
this to Charl.
On Mon, Dec 2, 2024 at 2:13 PM Barna Zajzon <b.zajzon(a)fz-juelich.de> wrote:
Dear Peter,
just a minor addition to Charl's observations: your pseudocode is missing
the scaling by the target weight w_target , which is present in the
example and may be wanted depending on your application.
Best,
Barna
On 02.12.24 11:44, Charl Linssen wrote:
Hi Peter,
In the page on weight normalisation (
https://nest-simulator.readthedocs.io/en/v3.8/synapses/weight_normalization…),
an example is shown that normalises the L1-norm of the vector. Indeed, it
divides by sum(abs(w)). So after the normalisation step, |w| = 1. I don't
know where the number 420 comes from, perhaps you can check your code on a
more simple example with only one neuron (or send us a minimal reproducing
code for the issue).
For your second query, please see:
https://nest-simulator.readthedocs.io/en/latest/auto_examples/store_restore…
Hope this helps!
With kind regards,
Charl
On Sun, Dec 1, 2024, at 21:00, Peter Mason wrote:
I am currently working on a project involving synaptic weight
normalization using the guidelines provided in the NEST simulator
documentation. I have implemented the normalization process; however, I
encountered some questions that I would appreciate your insights on.
1.
Normalization Value: I found that the normalization value for a neuron
with approximately 190 synapses is around 420, which I do not fully
understand. This looks like the total weight of the neuron synapses. Could
you provide clarification on how the normalisation value is determined?
2.
Simulation State Preservation: I would like to save and restore the
state of synaptic weights to maintain the simulation's behaviour across
sessions. Below is the pseudocode I intend to use:
Save synaptic weights:
w = array of current weights of neuron connections
normalization_factor = sum of absolute weights
if normalization_factor != 0:
normalized_weights = w / normalization_factor
save normalized_weights to file
Load synaptic weights:
read normalized_weights from file
assign loaded weights back to connection
I would like to know if you have any suggestions for improving this
pseudocode or if there are best practices I should consider.
Thank you for your time and assistance. I look forward to your response.
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--
Barna Zajzon, Postdoctoral Researcher
Institute for Advanced Simulation (IAS-6)
Computational and Systems Neuroscience
Jülich Research Centre,
Jülich, Germany
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