Synaptic transmission is a central component of neural processing. Short term depression occurs when repeated driving of a synapse reduces its efficacy, a feature that is common across the nervous system. The mechanics of synaptic discharge are well characterized and involve both probabilistic release and uptake of neurotransmitter during activity. However, many studies which consider the impact of depression on information flow use a deterministic model of depression, based on trial averaged response. Using techniques from stochastic calculus we derive simplified expressions for the information flow across a synapse which account for fluctuations in neurotransmitter release. We show that the timescales of the stochastic synaptic dynamics impart high pass information filtering that is absent in deterministic models. We expand our work to consider how synaptic depression affects the flow of correlated activity in networks of neurons. This is joint work with Robert Rosenbaum and Jonathan Rubin.