When more than one homeostatic process is at work in a given system, there is always a chance the two will compete with each other. For example, if two thermostats in a room control different heating/cooling systems, unless their set-points are identical, one could be causing heating and the other cooling while the temperature lies intermediate between the two set-points. In neurons, multiple homeostatic mechanisms appear to be at play. We focus on two categories, synaptic scaling, which impacts connection strengths between neurons, and intrinsic homeostasis, which impacts the excitability of neurons. We find criteria for the two homeostatic mechanisms to cooperate and produce a stable state of time-averaged neural activity. We show that in these conditions the neuron can respond to stimuli more reliably, because the variance as well as the mean of its firing rate can be controlled with two sensors. Finally, we demonstrate that within a recurrent feedback circuit, a useful computational operation--temporal integration of inputs--can be achieved by these dual control mechanisms operating together, i. e., they robustly achieve the fine-tuning of the circuit that is necessary.