The mammalian retina contains about 80 types of nerve cell, from the photoreceptors in the input layer to the ganglion cells in the output layer. The ganglion cells alone comprise about 30 different types. Each type tiles the visual field and is responsible for communicating one particular image feature to the rest of the brain. So we can think of the retina as 30 different visual processing circuits all interleaved in one network . Our goal is to understand their function by answering these three questions: What are the features each ganglion cell computes [2-3]? How is that done with the circuit elements of the retina [4-5]? Why does Nature prefer these computations over others [6-7]? Given the unsurpassed experimental access that the retina offers to input signals, output signals, interneurons, and synapses, this has been a particularly fertile area of dynamic neuroscience.
 Roska, M, Meister, M (2014) The Retina Dissects the Visual Scene into Distinct Features. In: The New Visual Neurosciences (Werner, JS, Chalupa, LM, eds), pp 163–182. Cambridge, MA: MIT Press.
 Zhang, Y, Kim, IJ, Sanes, JR, Meister, M (2012) The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc Natl Acad Sci U S A 109:E2391–8. doi: 10.1073/pnas.1211547109.
 Joesch, M, Meister, M (2016) A neuronal circuit for colour vision based on rod-cone opponency. Nature 532:236–239. doi:10.1038/nature17158.
 Baccus, SA, Ölveczky, BP, Manu, M, Meister, M (2008) A retinal circuit that computes object motion. J Neurosci 28:6807–6817. doi:10.1523/JNEUROSCI.4206-07.2008.
 Asari, H, Meister, M (2012) Divergence of visual channels in the inner retina. Nat Neurosci 15:1581–1589. doi:10.1038/nn.3241.
 Hosoya, T, Baccus, SA, Meister, M (2005) Dynamic predictive coding by the retina. Nature 436:71–77. doi:10.1038/nature03689.
 Leonardo, A, Meister, M (2013) Nonlinear dynamics support a linear population code in a retinal target-tracking circuit. J Neurosci 33:16971–16982. doi: 10.1523/JNEUROSCI.2257-13.2013.