  Real-time analysis and classification of bio-signals measured using wearable
devices is computationally costly and requires dedicated low-power hardware.
One promising approach is to use spiking neural networks implemented using
in-memory computing architectures and neuromorphic electronic circuits.
However, as these circuits process data in streaming mode without the
possibility of storing it in external buffers, a major challenge lies in the
processing of spatio-temporal signals that last longer than the time constants
present in the network synapses and neurons. Here we propose to extend the
memory capacity of a spiking neural network by using parallel delay chains. We
show that it is possible to map temporal signals of multiple seconds into
spiking activity distributed across multiple neurons which have time constants
of few milliseconds. We validate this approach on an ECG anomaly detection task
and present experimental results that demonstrate how temporal information is
properly preserved in the network activity.
