Biologically-inspired on-chip learning in pulsed neural networks

Self-learning chips to implement many popular ANN (artificial neural networ k) algorithms are very difficult to design. We explain why this is so and s ay what lessons previous work teaches us in the design of self-learning sys tems. We offer a contribution to the "biologically-inspired" approach, expl aining what we mean by this term and providing an example of a robust, self -learning design that can solve simple classical-conditioning tasks, We giv e details of the design of individual circuits to perform component functio ns, which can then be combined into a network to solve the task. We argue t hat useful conclusions as to the future of on-chip learning can be drawn fr om this work.