  Analog tunable memristors are widely utilized as artificial synapses in
various neural network applications. However, exploiting the dynamical aspects
of their conductance change to implement active neurons is still in its
infancy, awaiting the realization of efficient neural signal recognition
functionalities. Here we experimentally demonstrate an artificial neural
information processing unit that can detect a temporal pattern in a very noisy
environment, fire a single output spike upon successful detection and reset
itself in a fully unsupervised, autonomous manner. This circuit relies on the
dynamical operation of only two memristive blocks: a non-volatile Ta$_2$O$_5$
device and a volatile VO$_2$ unit. A fading functionality with exponentially
tunable memory time constant enables adaptive operation dynamics, which can be
tailored for the targeted temporal pattern recognition task. In the trained
circuit false input patterns only induce short-term variations. In contrast,
the desired signal activates long-term memory operation of the non-volatile
component, which triggers a firing output of the volatile block.
