The statistical structure of the environment is often important when making decisions. There are multiple theories of how
the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability
distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from
the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary
prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based
upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons
and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations
and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing
learning.