Methods and apparatus are provided for implementing behavioral homeostasis in artificial neurons that use a dynamical spiking neuron model. The homeostatic mechanism may be driven by neuron state, rather than by neuron spiking rate, and this mechanism may drive changes to the neuron temporal dynamics, rather than to contributions of input or weights. As a result, certain aspects of the present disclosure are a more natural fit with spiking neural networks and have many functional and computational advantages. One example method for implementing homeostasis of an artificial nervous system generally includes determining one or more state variables of a neuron model used by an artificial neuron, based at least in part on dynamics of the neuron model; determining one or more conditions based at least in part on the state variables; and adjusting the dynamics based at least in part on the conditions.