In this paper, we present a spiking neural model of life span inference. Through this model, we explore the biological plausibility of performing Bayesian computations in the brain. Specifically, we address the issue of representing probability distributions using neural circuits and combining them in meaningful ways to perform inference. We show that applying these methods to the life span inference task matches human performance on this task better than an ideal Bayesian model due to the use of neuron tuning curves. We also describe potential ways in which humans might be generating the priors needed for this inference. This provides an initial step towards better understanding how Bayesian computations may be implemented in a biologically plausible neural network.