On-chip learning in pulsed silicon neural networks

Abstract

Self-learning chips to implement conventional ANN (artificial neural network) algorithms are very difficult to design and unconvincing in their results. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer an alternative, 'biologically-inspired' approach, explaining what we mean by this term and providing an example of a robust, self-learning design which can solve simple classical-conditioning tasks.