Sean D. Murphy, Edward W. Kairiss
A biological neuron can be viewed as a device that maps a multidimen(cid:173) sional temporal event signal (dendritic postsynaptic activations) into a unidimensional temporal event signal (action potentials). We have designed a network, the Spatio-Temporal Event Mapping (STEM) architecture, which can learn to perform this mapping for arbitrary bio(cid:173) physical models of neurons. Such a network appropriately trained, called a STEM cell, can be used in place of a conventional compartmen(cid:173) tal model in simulations where only the transfer function is important, such as network simulations. The STEM cell offers advantages over compartmental models in terms of computational efficiency, analytical tractabili1ty, and as a framework for VLSI implementations of biologi(cid:173) cal neurons.