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.

Hi Peter,

In the page on weight normalisation (https://nest-simulator.readthedocs.io/en/v3.8/synapses/weight_normalization.html), 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_network.html

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.
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?
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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