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There has been significant research over the past two decades in developingnew platforms for spiking neural computation. Current neural computers areprimarily developed to mimick biology. They use neural networks which can betrained to perform specific tasks to mainly solve pattern recognition problems.These machines can do more than simulate biology, they allow us to re-think ourcurrent paradigm of computation. The ultimate goal is to develop brain inspiredgeneral purpose computation architectures that can breach the currentbottleneck introduced by the Von Neumann architecture. This work proposes a newframework for such a machine. We show that the use of neuron like units withprecise timing representation, synaptic diversity, and temporal delays allowsus to set a complete, scalable compact computation framework. The presentedframework provides both linear and non linear operations, allowing us torepresent and solve any function. We show usability in solving real use casesfrom simple differential equations to sets of non-linear differential equationsleading to chaotic attractors.

STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and Synchrony