  Biological neurons can detect complex spatio-temporal features in spiking
patterns via their synapses spread across across their dendritic branches. This
is achieved by modulating the efficacy of the individual synapses, and by
exploiting the temporal delays of their response to input spikes, depending on
their position on the dendrite. Inspired by this mechanism, we propose a
neuromorphic hardware architecture equipped with multiscale dendrites, each of
which has synapses with tunable weight and delay elements. Weights and delays
are both implemented using Resistive Random Access Memory (RRAM). We exploit
the variability in the high resistance state of RRAM to implement a
distribution of delays in the millisecond range for enabling spatio-temporal
detection of sensory signals. We demonstrate the validity of the approach
followed with a RRAM-aware simulation of a heartbeat anomaly detection task. In
particular we show that, by incorporating delays directly into the network, the
network's power and memory footprint can be reduced by up to 100x compared to
equivalent state-of-the-art spiking recurrent networks with no delays.
