When a sensory system constructs a model of the environment from its input, it might need to verify the model's accuracy. One method of verification is multivariate time-series prediction: a good model could predict the near-future activity of its inputs, much as a good scientific theory predicts future data. Such a predict(cid:173) ing model would require copious top-down connections to compare the predictions with the input. That feedback could improve the model's performance in two ways: by biasing internal activity to(cid:173) ward expected patterns, and by generating specific error signals if the predictions fail. A proof-of-concept model-an event-driven, computationally efficient layered network, incorporating "cortical" features like all-excitatory synapses and local inhibition- was con(cid:173) structed to make near-future predictions of a simple, moving stim(cid:173) ulus. After unsupervised learning, the network contained units not only tuned to obvious features of the stimulus like contour orienta(cid:173) tion and motion, but also to contour discontinuity ("end-stopping") and illusory contours.