  Whilst most engineered systems use signals that are continuous in time, there
is a domain of systems in which signals consist of events. Events, like Dirac
delta functions, have no meaningful time duration. Many important real-world
systems are intrinsically event-based, including the mammalian brain, in which
the primary packets of data are spike events, or action potentials. In this
domain, signal processing requires responses to spatio-temporal patterns of
events. We show that some straightforward modifications to the standard ELM
topology produce networks that are able to perform spatio-temporal event
processing online with a high degree of accuracy. The modifications involve the
re-definition of hidden layer units as synaptic kernels, in which the input
delta functions are transformed into continuous-valued signals using a variety
of impulse-response functions. This permits the use of linear solution methods
in the output layer, which can produce events as output, if modeled as a
classifier; the output classes are 'event' or 'no event'. We illustrate the
method in application to a spike-processing problem.
