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Spatial navigation in mammals is based on building a mental representation of their environment—a cognitive map. However, both the nature of this map and the mechanisms responsible for producing it remain vague. In this paper, we explore different implementations (schemas) of the topological hypothesis using different mathematical approaches and demonstrate that each schema provides its own large-scale characteristics of the environment. Here we also provide a theoretical and computational differentiation between the cognitive (system level) and the physiological (network level) aspects of spatial learning.

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This page is a summary of: Topological Schemas of Cognitive Maps and Spatial Learning, Frontiers in Computational Neuroscience, March 2016, Frontiers,
DOI: 10.3389/fncom.2016.00018.
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