  In this paper, we infer ingrained remote information in AC power flows using
spiking neural network (SNN) as edge processors for efficient coordination of
power electronic converters. This work unifies power and information as a means
of data normalization using a multi-modal regime in the form of spikes using
energy-efficient neuromorphic processing and semantics theory. Firstly, we
organize the synchronous realvalued measurements at each edge and translate
them into asynchronous spike-based events to collect sparse data for training
of SNN at each edge. Instead of relying on error-dependent supervised
data-driven learning theory, we exploit the latency-driven unsupervised Hebbian
learning rule to obtain modulation pulses for switching of power electronic
converters that can now communicate among each other. Not only does this
philosophy block exogenous path arrival for cyber attackers by dismissing the
cyber layer, it also entails converter adaptation to system reconfiguration and
parameter mismatch issues. We conclude this work by validating its
energy-efficient and effective online learning performance under various
scenarios in modified IEEE 14-bus system and under experimental conditions.
