n explain a wide variety of seemingly unrelated phenomena observed in visual cortex and visual perception. These phenomena range from the details of responses of individual neurons, to complex visual illusions. Importantly, throughout, we used a base model trained on natural videos. Our work adds to a growing body of literature showing that deep neural networks trained to perform relevant tasks can serve as surprisingly good models of biological neural networks, often even outperforming models designed to explain neuroscience phenomena.

While we have shown that the PredNet architecture demonstrates a wide range of phenomena reminiscent of biology, we do not claim that the PredNet architecture *per se* is required to explain these phenomena. Rather, we argue that the network is *sufficient* to produce these phenomena, and we note that explicit representation of prediction errors in units within the feedforward path of the PredNet provides a straightforward explanation for the transient nature of responses in visual cortex in response to static images. That a single, simple objective—prediction—can produce such a wide variety of observed neural phenomena underscores the idea that prediction may be a central organizing principle in the brain [@Rao_1999], and points toward fruitful directions for future study in both neuroscience and machine learning.

### Acknowledgments {#acknowledgments .unnumbered}

This work was supported by IARPA (contract D16PC00002), the National Science Foundation (NSF IIS 1409097), and the Center for Brains, Minds and Machines (CBMM, NSF STC award CCF-1231216).

Supplementary Material
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On/Off Temporal Dynamics
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Temporal dynamics were tested with a set of $25$ objects. Examples of the objects can be seen in Fig. \[image\_pairing\_examples\]. Testing sequences consisted of a gray background for $7$ time steps, followed by an object on the background for $6$ time steps. As a general theme, we see some diversity in the response profiles of a