Due to their distributed architecture, artificial neural networks often show a graceful performance degradation to the loss of few units or connections. Living systems also display an additional source of fault-tolerance obtained through distributed processes of self-healing: defective components are actively regenerated. In this paper, we present results obtained with a model of development for spiking neural networks undergoing sustained levels of cell loss. To test their resistance to faults, networks are subjected to random faults during development and mutilated several times during operation. Results show that, evolved to control simulated Khepera robots in a simple navigation task, plastic and non-plastic networks develop fault-tolerant structures which can recover normal operation to various degrees.