The Role of Idiothetic Signals, Landmarks, and Conjunctive Representations in the Development of Place and Head-Direction Cells: A Self-Organizing Neural Network Model

Author:

St. Clere Smithe Toby1ORCID,Stringer Simon M1

Affiliation:

1. Department of Experimental Psychology, Centre for Theoretical Neuroscience and Artificial Intelligence, University of Oxford, Oxford OX2 6NW, UK

Abstract

Abstract Place and head-direction (HD) cells are fundamental to maintaining accurate representations of location and heading in the mammalian brain across sensory conditions, and are thought to underlie path integration—the ability to maintain an accurate representation of location and heading during motion in the dark. Substantial evidence suggests that both populations of spatial cells function as attractor networks, but their developmental mechanisms are poorly understood. We present simulations of a fully self-organizing attractor network model of this process using well-established neural mechanisms. We show that the differential development of the two cell types can be explained by their different idiothetic inputs, even given identical visual signals: HD cells develop when the population receives angular head velocity input, whereas place cells develop when the idiothetic input encodes planar velocity. Our model explains the functional importance of conjunctive “state-action” cells, implying that signal propagation delays and a competitive learning mechanism are crucial for successful development. Consequently, we explain how insufficiently rich environments result in pathology: place cell development requires proximal landmarks; conversely, HD cells require distal landmarks. Finally, our results suggest that both networks are instantiations of general mechanisms, and we describe their implications for the neurobiology of spatial processing.

Funder

Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence

Publisher

Oxford University Press (OUP)

Subject

General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Neural Network Model of Self-Organized Learning in Hippocampus- Entorhinal Cortex Spatial Representations;2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC);2023-10-20

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