2 Dec
2024
2 Dec
'24
1:47 p.m.
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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