A self-organizing developmental framework for the head direction ring attractor
- 1School of Systems Science, Beijing Normal University, Beijing, China
- 2School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- 3Chinese Institute for Brain Research, Beijing, 102206 China
Abstract
The head direction (HD) system provides a continuous representation of the animal’s facing direction and is a core component of the spatial navigation network. Although mature HD circuits have been studied extensively, their developmental origin remains unclear, particularly because young animals already show attractor-like internal coherence before stable visual anchoring is established. Here we propose a developmental computational framework of the lateral mammillary nucleus and dorsal tegmental nucleus (LMN–DTN) circuit, in which a functional HD ring attractor emerges through staged self-organization without preconfigured topology or early external supervision. In the model, a randomly connected LMN network first generates stable travelling waves through relative refractoriness. These waves provide a temporal scaffold for subsequent learning. Local STDP-like and structural plasticity then consolidate wave-based statistics into locally enhanced recurrent connectivity, followed by direction-selective shift-feedback learning in the DTN→ LMN pathway. At a later stage, recruited long-range inhibition transforms the travelling-wave manifold into a stable single activity profile (bump) attractor. Visual input acts mainly as a late calibration signal for global integration gain: it improves short-timescale path integration, but does not remove long-term drift. The framework provides a testable account of the developmental dissociation between early internal organization and later sensory anchoring. More broadly, it suggests that key components of classical ring attractor models, including local recurrent structure, direction-selective shift feedback, and late stabilizing mechanisms, can arise through a staged developmental process rather than relying on a preconfigured network architecture.
Related
- Active locomotion predictively rescues head direction attractor dynamics in head-fixed mice
- The degree and direction of subgenome expression bias in Brassica napus vary between cultivars, tissues and developmental stages
- Prospective Coding and Path Integration Emerge as Equilibrium Solutions of Self-Organizing Neural Networks with Firing-Rate Adaptation
- Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids
- AMADEUS: Annotation-free multi-animal direction estimation using self-supervised learning
BibTeX
@article{liu2026selforganizing,
title = {{A self-organizing developmental framework for the head direction ring attractor}},
author = {Shujia Liu and Bailu Si and J. Michael Herrmann},
journal = {Journal of Computational Neuroscience},
year = {2026},
month = oct,
doi = {10.1007/s10827-026-00955-4},
url = {https://doi.org/10.1007/s10827-026-00955-4},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A self-organizing developmental framework for the head direction ring attractor
AU - Shujia Liu
AU - Bailu Si
AU - J. Michael Herrmann
PY - 2026
DA - 2026/10/17
JO - Journal of Computational Neuroscience
DO - 10.1007/s10827-026-00955-4
UR - https://doi.org/10.1007/s10827-026-00955-4
AB - The head direction (HD) system provides a continuous representation of the animal’s facing direction and is a core component of the spatial navigation network. Although mature HD circuits have been studied extensively, their developmental origin remains unclear, particularly because young animals already show attractor-like internal coherence before stable visual anchoring is established. Here we propose a developmental computational framework of the lateral mammillary nucleus and dorsal tegmental nucleus (LMN–DTN) circuit, in which a functional HD ring attractor emerges through staged self-organization without preconfigured topology or early external supervision. In the model, a randomly connected LMN network first generates stable travelling waves through relative refractoriness. These waves provide a temporal scaffold for subsequent learning. Local STDP-like and structural plasticity then consolidate wave-based statistics into locally enhanced recurrent connectivity, followed by direction-selective shift-feedback learning in the DTN→ LMN pathway. At a later stage, recruited long-range inhibition transforms the travelling-wave manifold into a stable single activity profile (bump) attractor. Visual input acts mainly as a late calibration signal for global integration gain: it improves short-timescale path integration, but does not remove long-term drift. The framework provides a testable account of the developmental dissociation between early internal organization and later sensory anchoring. More broadly, it suggests that key components of classical ring attractor models, including local recurrent structure, direction-selective shift feedback, and late stabilizing mechanisms, can arise through a staged developmental process rather than relying on a preconfigured network architecture.
SN - 0929-5313
SN - 1573-6873
ER -