  Time-encoded signals, such as social network update logs and spiking traces
in neuromorphic processors, are defined by multiple traces carrying information
in the timing of events, or spikes. When time-encoded data is processed at a
remote site with respect to the location it is produced, the occurrence of
events needs to be encoded and transmitted in a timely fashion. The standard
Address-Event Representation (AER) protocol for neuromorphic chips encodes the
indices of the "spiking" traces in the payload of a packet produced at the same
time the events are recorded, hence implicitly encoding the events' timing in
the timing of the packet. This paper investigates the potential bandwidth
saving that can be obtained by carrying out variable-length compression of
packets' payloads. Compression leverages both intra-trace and inter-trace
correlations over time that are typical in applications such as social networks
or neuromorphic computing. The approach is based on discrete-time Hawkes
processes and entropy coding with conditional codebooks. Results from an
experiment based on a real-world retweet dataset are also provided.
