  Spiking neural networks (SNNs) could play a key role in unsupervised machine
learning applications, by virtue of strengths related to learning from the fine
temporal structure of event-based signals. However, some spike-timing-related
strengths of SNNs are hindered by the sensitivity of spike-timing-dependent
plasticity (STDP) rules to input spike rates, as fine temporal correlations may
be obstructed by coarser correlations between firing rates. In this article, we
propose a spike-timing-dependent learning rule that allows a neuron to learn
from the temporally-coded information despite the presence of rate codes. Our
long-term plasticity rule makes use of short-term synaptic fatigue dynamics. We
show analytically that, in contrast to conventional STDP rules, our fatiguing
STDP (FSTDP) helps learn the temporal code, and we derive the necessary
conditions to optimize the learning process. We showcase the effectiveness of
FSTDP in learning spike-timing correlations among processes of different rates
in synthetic data. Finally, we use FSTDP to detect correlations in real-world
weather data from the United States in an experimental realization of the
algorithm that uses a neuromorphic hardware platform comprising phase-change
memristive devices. Taken together, our analyses and demonstrations suggest
that FSTDP paves the way for the exploitation of the spike-based strengths of
SNNs in real-world applications.
