The conventional interpretation of spikes is from the perspective of an external observer with knowledge of a neuron’s inputs and outputs who is ignorant of the contents of the black box that is the neuron. Here we consider a neuron to be an observer and we interpret spikes from the neuron’s perspective. We propose both a descriptive hypothesis based on physics and logic, and a prescriptive hypothesis based on biological optimality. Our descriptive hypothesis is that a neuron’s membrane excitability is known and the amplitude of a future excitatory postsynaptic conductance (EPSG) is unknown. Therefore excitability is an expectation of EPSG amplitude and a spike is generated only when EPSG amplitude exceeds its expectation (prediction error). Our prescriptive hypothesis is that a diversity of synaptic inputs and voltage-regulated ion channels implement predictive homeostasis, working to insure that the expectation is accurate. The homeostatic ideal and optimal expectation would be achieved when an EPSP reaches precisely to spike threshold, so that spike output is exquisitely sensitive to small variations in EPSG input. To an external observer who knows neither EPSG amplitude nor membrane excitability, spikes would appear random if the neuron is making accurate predictions. We review experimental evidence that spike probabilities are indeed maintained near an average of 0.5 under natural conditions, and we suggest that the same principles may also explain why synaptic vesicle release appears to be stochastic. Whereas the present hypothesis accords with principles of efficient coding dating back to Barlow (1961), it contradicts decades of assertions that neural activity is substantially random or noisy. The apparent randomness is by design, and like many other examples of apparent randomness, it corresponds to the ignorance of external macroscopic observers about the detailed inner workings of a microscopic system.