Algorithm 1. The dynamic spatiotemporal clustering algorithm at time point t of the unsupervised learning process.

Input: Input spike data $\mathit{s}\mathit{p}$, number of neurons in the SNN model $\mathit{N}$, number of input variables $\mathit{v}$, connection weights $\mathit{w}\left[\mathit{N},\mathit{N}\right]$, and parameter $\mathit{\alpha }$, $\mathit{P}\mathit{S}\mathit{P},$ STDP, time $\mathit{t}$ Output: A vector of labelled neurons k, vector of spik events for each cluster 1: Procedure 2: $\left[\mathit{L} \mathit{V}\right]=\mathit{s}\mathit{i}\mathit{z}\mathit{e}\left(\mathit{s}\mathit{p}\right)$ 3: $Fsrc \in {\mathit{R}}^{\mathit{N}\times \mathit{v}}$, $A\in {\mathit{R}}^{\mathit{N}\times \mathit{N}}$ 4: For each time point t from the input stream data Do 5: $Update \mathit{w} with STDP$ 6: $\mathit{S}=\mathit{D} A \mathit{D}$ 7: ${\mathit{F}}^{*}={\left(\mathit{I}-\mathit{\alpha }\mathit{S}\right)}^{-1} {\mathit{F}}_{\mathit{s}\mathit{r}\mathit{c}}$ 8: $\mathit{k}=\mathit{a}\mathit{r}\mathit{g} \mathit{m}\mathit{a}{\mathit{x}}_{\mathit{j}=\mathbf{1},\dots \mathit{v}} {\mathit{F}}^{*}{}_{\mathit{i}\mathit{j}}$ 9: Visualization of the clusters 10: Spatiotemporal rules within each cluster Do 11: If $\mathit{P}\mathit{S}\mathit{P}\left(\mathit{t}\right)\ge \mathit{e}\mathit{v}\mathit{e}\mathit{n}\mathit{t}-\mathit{t}\mathit{h}\mathit{r}\mathit{e}\mathit{s}\mathit{h}\mathit{o}\mathit{l}\mathit{d}$ 12: Cluster fires as active event in time $\mathit{t}$. 13: End if 14: End for 15: Algorithms to generate a set of spatiotemporal rules 16: End of procedure