  Neuromorphic computing is emerging to be a disruptive computational paradigm
that attempts to emulate various facets of the underlying structure and
functionalities of the brain in the algorithm and hardware design of
next-generation machine learning platforms. This work goes beyond the focus of
current neuromorphic computing architectures on computational models for neuron
and synapse to examine other computational units of the biological brain that
might contribute to cognition and especially self-repair. We draw inspiration
and insights from computational neuroscience regarding functionalities of glial
cells and explore their role in the fault-tolerant capacity of Spiking Neural
Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent
Plasticity (STDP). We characterize the degree of self-repair that can be
enabled in such networks with varying degree of faults ranging from 50% - 90%
and evaluate our proposal on the MNIST and Fashion-MNIST datasets.
