Why is the visual system organized the way it is, as opposed to all the alternatives we could imagine? Here the explanations are ultimately evolutionary in character. Theory and modeling allow one to understand how the constraints from the physical environment together with the capabilities of biological hardware have shaped the solutions we find among animals today. We focus on aspects that generalize across species and brain systems: Why do sensory signals split into many parallel pathways early on ? Why are the eyes always in motion , and how does that enable or constrain neural computation [3-4]? What explains the spatio-temporal filters one finds at early sensory stages [5-6]? How might the brain learn to navigate a new environment ?
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 Pitkow, X, Sompolinsky, H, Meister, M (2007) A neural computation for visual acuity in the presence of eye movements. PLoS Biol 5:e331. doi:10.1371/journal.pbio.0050331.
 Burak, Y, Rokni, U, Meister, M, Sompolinsky, H (2010) Bayesian model of dynamic image stabilization in the visual system. PNAS 107:19525–19530. doi: 10.1073/pnas.1006076107 .
 Gütig, R, Gollisch, T, Sompolinsky, H, Meister, M (2013) Computing complex visual features with retinal spike times. PLoS ONE 8:e53063. doi:10.1371/journal.pone.0053063 .
 Pitkow, X, Meister, M (2012) Decorrelation and efficient coding by retinal ganglion cells. Nat Neurosci 15:628–635. doi:10.1038/nn.3064.
 Pitkow, X, Meister, M (2014) Neural computation in sensory systems. In: The Cognitive Neurosciences (Gazzaniga, MS, Mangun, GR, eds), pp 305–318. Cambridge, MA: MIT Press.
 Zhang, T., Rosenberg, M., Perona, P., and Meister, M. (2021). Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation. https://www.biorxiv.org/content/10.1101/2021.09.24.461751v1