ELM solutions for event-based systems

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 spatia-temporal patterns of events. We show that some straightforward modifications to the standard ELM topology produce networks that are able to perform spatiatemporal 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.