Place fields in the hippocampus show a variety of remapping phenomena in response to environmental changes. These remapping phenomena get characterized in terms of different types of coding of spatial information — object vector cells, landmark vector cells, distance coding, etc. But what if these phenomena are side effects of mapping hippocampal neuronal responses on to Euclidean maps?
In this talk I will describe how treating space as a sequence can resolve many of the confusing phenomena that are ascribed to spatial remapping. Using our Clone-Structured Cognitive Graphs (CSCG) model , we’ll show how allocentric “spatial” representations naturally arise from higher-order sequence learning on ordinal egocentric sensory inputs, without making any Euclidean assumptions, and without having locations as an input. An organism can utilize CSCG for navigation, foraging, context-recognizing, etc. without ever having to compute place fields, or having to think about locations. A wide variety of spatial and temporal phenomena — splitter cells, boundary/landmark/object vector coding, event-specific representations, transitive learning and inference, partial remapping, rate remapping, varying sizes of place fields, place field repetition, etc. — have succinct mechanistic explanations in CSCG using the simple principle of latent higher-order sequence learning. Importantly, I’ll show how many of these observed phenomena are purely side effects of mapping neural responses to a Euclidean space, and describe why care should be taken to not over-interpret place field phenomena.
CSCGs can also offer explanations for schema formation, short-cut finding in novel environments, and different kinds of offline and online hippocampal replay. Overall latent sequence learning using graphical representations might provide a unifying framework for understanding hippocampal function, and could be a pathway for forming temporal and relational abstractions in artificial intelligence.
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