  Spiking neural networks implemented in dynamic neuromorphic processors are
well suited for spatiotemporal feature detection and learning, for example in
ultra low-power embedded intelligence and deep edge applications. Such pattern
recognition networks naturally involve a combination of dynamic delay
mechanisms and coincidence detection. Inspired by an auditory feature detection
circuit in crickets, featuring a delayed excitation by postinhibitory rebound,
we investigate disynaptic delay elements formed by inhibitory-excitatory pairs
of dynamic synapses. We configure such disynaptic delay elements in the
DYNAP-SE neuromorphic processor and characterize the distribution of delayed
excitations resulting from device mismatch. Furthermore, we present a network
that mimics the auditory feature detection circuit of crickets and demonstrate
how varying synapse weights, input noise and processor temperature affects the
circuit. Interestingly, we find that the disynaptic delay elements can be
configured such that the timing and magnitude of the delayed postsynaptic
excitation depend mainly on the efficacy of the inhibitory and excitatory
synapses, respectively. Delay elements of this kind can be implemented in other
reconfigurable dynamic neuromorphic processors and opens up for synapse level
temporal feature tuning with large fan-in and flexible delays of order 10-100
ms.
