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weight_recorder – Recording weights from synapses


The change in synaptic weights over time is a key observable property in studies of plasticity in neuronal network models. To access this information, the weight_recorder can be used. In contrast to other recording devices, which are connected to a specific set of neurons, the weight recorder is instead set as a parameter in the synapse model.

After assigning an instance of a weight recorder to the synapse model by setting its weight_recorder property, the weight recorder collects the global IDs of source and target neurons together with the weight for each spike event that travels through the observed synapses.

To only record from a subset of connected synapses, the weight recorder accepts NodeCollections in the parameters senders and targets. If set, they restrict the recording of data to only synapses that fulfill the given criteria.

>>> wr = nest.Create('weight_recorder')
>>> nest.CopyModel("stdp_synapse", "stdp_synapse_rec", {"weight_recorder": wr})

>>> pre = nest.Create("iaf_psc_alpha", 10)
>>> post = nest.Create("iaf_psc_alpha", 10)

>>> nest.Connect(pre, post, syn_spec="stdp_synapse_rec")