  Spiking Neural Networks (SNNs) have become an essential paradigm in
neuroscience and artificial intelligence, providing brain-inspired computation.
Recent advances in literature have studied the network representations of deep
neural networks. However, there has been little work that studies
representations learned by SNNs, especially using unsupervised local learning
methods like spike-timing dependent plasticity (STDP). Recent work by
\cite{barannikov2021representation} has introduced a novel method to compare
topological mappings of learned representations called Representation Topology
Divergence (RTD). Though useful, this method is engineered particularly for
feedforward deep neural networks and cannot be used for recurrent networks like
Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to
measure the difference between distributed representations of RSNN models with
different learning methods. We propose a novel reformulation of RSNNs using
feedforward autoencoder networks with skip connections to help us compute the
RTD for recurrent networks. Thus, we investigate the learning capabilities of
RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics
in learning such representations. We demonstrate that heterogeneous STDP in
RSNNs yield distinct representations than their homogeneous and surrogate
gradient-based supervised learning counterparts. Our results provide insights
into the potential of heterogeneous SNN models, aiding the development of more
efficient and biologically plausible hybrid artificial intelligence systems.
