Many models have postulated that the neocortex implements hierarchical inference system, whereby each region sends predictions of the inputs it expects to lower-order regions, allowing the latter to learn from any prediction errors. The combining of top-down predictions with bottom-up sensory information to generate errors that can then be communicated across the hierarchy is critical to credit assignment in deep predictive learning algorithms. Indirect experimental evidence supporting a hierarchical prediction system in the neocortex comes from both human and animal work. However, direct evidence for top-down guided prediction errors in the neocortex that can be used for deep credit assignment during unsupervised learning remains limited. Here, we address this issue with 2-photon calcium imaging of layer 2/3 and layer 5 pyramidal neurons in the primary visual cortex of awake mice during passive exposure to visual stimuli where unexpected events occur. To assess the evidence for top-down guided prediction errors we recorded from both the somatic compartments, and the apical dendrites in layer 1, where a large number of top-down inputs are received. We find evidence for a diversity of prediction error signals depending on both the stimulus type and cell type. These signals can be learnt in some cases, and in turn, they appear to drive some learning. This data will help us to both understand hierarchical inference in the neocortex, and potentially guide new unsupervised techniques for machine learning.