  The discovery of neural plasticity has proved that throughout the life of a
human being, the brain reorganizes itself through forming new neural
connections. The formation of new neural connections are achieved through the
brain's effort to adapt to new environments or to changes in the existing
environment. Despite the realization of neural plasticity, there is a lack of
understanding the probability of neural plasticity occurring given some event.
Using ordinary differential equations, neural firing equations and spike-train
statistics, we show how an additive short-term memory (STM) equation can be
formulated to approach the computation of neural plasticity. We then show how
the additive STM equation can be used for probabilistic inference in computable
neural plasticity, and the computation of probabilistic neural plasticity. We
will also provide a brief introduction to the theory of probabilistic neural
plasticity and conclude with showing how it can be applied to multiple
disciplines such as behavioural science, machine learning, artificial
intelligence and psychiatry.
