  Learning synaptic weights of spiking neural network (SNN) models that can
reproduce target spike trains from provided neural firing data is a central
problem in computational neuroscience and spike-based computing. The discovery
of the optimal weight values can be posed as a supervised learning task wherein
the weights of the model network are chosen to maximize the similarity between
the target spike trains and the model outputs. It is still largely unknown
whether optimizing spike train similarity of highly recurrent SNNs produces
weight matrices similar to those of the ground truth model. To this end, we
propose flexible heuristic supervised learning rules, termed Pre-Synaptic Pool
Modification (PSPM), that rely on stochastic weight updates in order to produce
spikes within a short window of the desired times and eliminate spikes outside
of this window. PSPM improves spike train similarity for all-to-all SNNs and
makes no assumption about the post-synaptic potential of the neurons or the
structure of the network since no gradients are required. We test whether
optimizing for spike train similarity entails the discovery of accurate weights
and explore the relative contributions of local and homeostatic weight updates.
Although PSPM improves similarity between spike trains, the learned weights
often differ from the weights of the ground truth model, implying that
connectome inference from spike data may require additional constraints on
connectivity statistics. We also find that spike train similarity is sensitive
to local updates, but other measures of network activity such as avalanche
distributions, can be learned through synaptic homeostasis.
