Hello Stefan,
I am afraid that there is no way to implement such a global normalization efficiently in NEST. This is in a way a consequence of NEST's implementation reflecting biological
structure, where you also do not have global communication between the axonal processes of a neuron (although some global constraints due to energy an other supplies to the synapses, I presume).
At present, you would need to
- use GetConnections to obtain SynapeCollection(s) containing the synapses you are intersted in
- simulate in small time steps
- obtain the weights from the synapse collections, normalize them and set them on the synapse collection
Note that synaptic weights are updated only when a spike passes through a synapse, so the weight you read out is the weight at the time of the last spike that passed
a synapse.
Depending on the precise plasticity rule, one might also need to check whether the plasticity rule remains internally consistent when weights are manipulated externally.
If the rule has internal state variables that depend not only on spike timing but also on synaptic weights at earlier times, this might lead to problems.
Best regards,
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
Home http://arken.nmbu.no/~plesser