 The use of computational methods in neuroscience itself is not new. Hodgkin, Huxley, and Katz (1952) incorporated values of voltage-dependent sodium and potassium conductance they had measured experimentally in the squid giant axon into an equation from physics describing the time evolution of a first-order kinetic process. This equation enabled them to calculate best-fit curves for modeled conductance versus time data that reproduced the changing membrane potential over time when action potentials were generated. Also using equations borrowed from physics, Rall (1959) developed the cable model of dendrites. This model provided an account of how the various inputs from across the dendritic tree interact temporally and spatially to determine the input-output properties of single neurons. It remains influential today, and was incorporated into the GENESIS software for programming neurally realistic networks (Bower and Beeman 1995; see discussion in  section 2 above).  David Sparks and his colleagues showed that a vector-averaging model of activity in neurons of superior colliculi correctly predicts experimental results about the amplitude and direction of saccadic eye movements (Lee, Rohrer, and Sparks 1988). Working with a more sophisticated mathematical model, Apostolos Georgopoulos and his colleagues predicted direction and amplitude of hand and arm movements based on averaged activity of 224 cells in motor cortex. Their predictions were borne out under a variety of experimental tests (Georgopoulos, Schwartz, and Kettner 1986). We mention these particular studies only because these are ones with which we are familiar. No doubt we could multiply examples of the fruitful interaction of computational and experimental methods in neuroscience easily by one-hundred-fold. Many of these extend back before “computational neuroscience” was a recognized research endeavor.