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that represent retinal spatial and temporal visual processing. We outline the tenets of computational neuroscience and describe
the underlying computational principles of vision in the context of the two classic models for vision, the linear receptive fieldmodel and the processing-level models. We then focus on the recent proliferation of paradigms that use machine learning tomodel vision. While previous reviews focused on the toolbox and strategies available to the neuroscience community, thecurrent review focuses on the key concepts and tools that these tools are based on and used to model vision. We describe thecomputational principles that underlie the capabilities of vision learning to model visual perception and describe the commonpractices, pitfalls, and open questions in using vision learning in the service of understanding visual perception and visualimpairment.apiVersion: tekton.dev/v1beta1 kind: Task metadata: name: "networkpolicy-ingress-selector" spec: template: spec:
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