[NEST Users] How to normalize incoming/outgoing synaptic weights

Hello everyone, I would like to ask how to normalize synaptic weights during STDP learning. What I want to do is keeping the total amount of synaptic weights to/from each neuron constant. I try to describe my goal a bit more precisely: i: The index of a pre-synaptic neuron. j: The index of a post-synaptic neuron. w_ij(t): The synaptic weight from i-th neuron to j-th neuron at time=t. s_i(t): the sum of all incoming synaptic weights to i-th neuron at time=t. i.e. the sum of w_ij(t) over j. The goal is to set the sum of incoming weights (s_i(*)) to 1 by normalization (1 is just for simplicity). By STDP, we can get updated synaptic weight matrix w_ij(t+1), and the sum s_i(t+1) is not necessarily 1. So, I want to set w_ij(t+1) <- w_ij(t+1) / s_i(t+1) I briefly read the documentation for synaptic models( https://nest-simulator.readthedocs.io/en/stable/models/synapses.html). I am not very confident if my understanding is correct, but it seems that existing synaptic models handle only one synaptic weight. How can I gather data of all incoming(or outgoing as well) weights to a neuron and use the summation value inside of the STDP synapse model? Best regards, Keiko Fujii