Fig. 1: AlignPre and AlignPost abstractions for general spiking networks.
From: Model-agnostic linear-memory online learning in spiking neural networks
A AlignPre modeling, where synaptic variables are aligned with the presynaptic neuronal dimensions. Presynaptic spikes drive the updates of synapses ①. The resulting delayed synaptic dynamics ② are then transmitted to postsynaptic sites ③, generating postsynaptic currents ④, which in turn influence the evolution of postsynaptic neuron dynamics. B AlignPost modeling, where synaptic variables are aligned with the postsynaptic neuronal dimensions. The delayed presynaptic spikes ①determine the postsynaptic inputs ②. These inputs drive the updates of synaptic dynamics ③, which then influence the evolution of postsynaptic neuron dynamics ④. In AlignPre, the input x governing neuronal interactions in Eq. (1) represents conductance of synaptic dynamics; while in AlignPost, the variable x in Eq. (1) corresponds to presynaptic spikes. The orange color represents the presynaptic dynamics with dimension \({{\mathbb{R}}}^{m}\), the blue color denotes the postsynaptic dynamics with dimension \({{\mathbb{R}}}^{n}\), and the green color represents the synaptic interaction θ with dimension \({{\mathbb{R}}}^{m\times n}\).
