Adapting synaptic delay on postsynaptic spike

[mstimberg]

Hi again. Note that this topic is a bit of a duplicate of Delays as variables.

@adididi

Updating the whole set of synaptic delays of the network each T ms breaks the event nature of the delay learning rule, and may cause malfunction (e.g. if a neuron spike more than once during the time interval T). Because I don’t know the duration of the patterns that are fed in the network, the period between two updates of the delays shall be arbitrarily small.

I am not sure what biological mechanism you are modelling that is capable of changing a delay between two spikes – I imagine mechanisms that change delays to be rather slow since they involve structural changes in the axon or similar things. So what I had in mind was something akin to “batch learning” – summing up many very small changes over some time, and then only applying them when they become meaningful.

Another approach would be to not have a proper delay learning rule in the first place, but instead use several synapses with different delays and use a learning rule to select between them (as in this paper: A neuronal learning rule for sub-millisecond temporal coding | Nature)

@adididi

So are there any solution with Brian2 for this problem that guarantees reasonable simulation speed ?
If not, should I use a specific version of Brian to get the dynamic delay feature during runtime ?

Apart from my suggestions above, I am afraid no. And while some of this was in principle possible with Brian 1, there are so many things that it does not support (Python 3, C++ code generation, …) that I cannot recommend using it.

The only solution that would give you exactly what you want would be to work on Allow time varying delays in Synapses as an option · Issue #355 · brian-team/brian2 · GitHub – it is definitely on our list (and has been there for a long time), but we are severely limited in the time we can spend on this.