Spiking neural networks (SNNs) are investigated as biologically inspired
models of neural computation, distinguished by their computational capability
and energy efficiency due to precise spiking times and sparse spikes with
event-driven computation. A significant question is how SNNs can emulate
human-like graph-based reasoning of concepts and relations, especially
leveraging the temporal domain optimally. This paper reveals that SNNs, when
amalgamated with synaptic delay and temporal coding, are proficient in
executing (knowledge) graph reasoning. It is elucidated that spiking time can
function as an additional dimension to encode relation properties via a
neural-generalized path formulation. Empirical results highlight the efficacy
of temporal delay in relation processing and showcase exemplary performance in
diverse graph reasoning tasks. The spiking model is theoretically estimated to
achieve $20\times$ energy savings compared to non-spiking counterparts,
deepening insights into the capabilities and potential of biologically inspired
SNNs for efficient reasoning. The code is available at
this https URL

SNNs, when combined with synaptic delay and temporal coding, demonstrate efficient graph reasoning and achieve significant energy savings compared to non-spiking networks.