Introduction Most ring attractor models hard-code and phase-biased translation kernels to obtain a stable activity profile (bump) and velocity-driven shifts [1]. This bypasses a key developmental question: Can these stabilizing and translation kernels self-organize, without any pre-set ring topology, from activity statistics under staged multimodal constraints? The Lateral Mammillary Nucleus--Dorsal Tegmental Nucleus (LMN--DTN) loop implicated in head direction system also lacks developmental constraints. Motivated by synfire chain theory [2] we build a plasticity enabled LMN--DTN model and propose: Spontaneous traveling wave statistics plus staged vestibular/visual constraints can drive the emergence of a ring attractor and path integration.
Methods We constructed a rate-based LMN--DTN circuit model with 400 neurons in LMN and two populations of 400 direction--velocity conjunctive cells in DTN. LMN follows leaky integrator dynamics with plastic recurrent excitatory connectivity and a fixed long-range inhibitory kernel. DTN to LMN feedback consists of plastic phase-biased weights gated by the angular velocity input. Training proceeds in functional stages: We first obtain stable traveling wave statistics without external velocity or vision, then update connectivity via STDP-like and structural plasticity, and subsequently introduce long-range inhibition and a visual teacher for representational stabilization and gain
Results With random sparse connectivity, no external velocity input, and no hand-designed ring topology templates, LMN networks spontaneously produce a stable unidirectional traveling wave under the joint action of dominant refractory-like neuronal dynamics and global inhibition, exhibiting consistent phase progression (Fig. 1A). STDP-like and structural plasticity then consolidate the temporal correlations into locally enhanced recurrent excitation (Fig. 1B); long-range inhibition transforms the traveling wave regime into a phase-selectable single bump state. Visual relearning markedly improves short-term angle tracking, yet cumulative drift persists during pure path integration after removing visual information (Fig. 1C).
Discussion Our results indicate that stabilizing and translation kernels of ring attractors need not be hard-coded: intrinsic recurrent dynamics can provide a directional temporal scaffold, which activity-dependent plasticity, staged inhibition, and multimodal constraints shape into a stable bump representation and a learnable translation kernel. Although residual drift remains after visual removal, it is structured rather than arbitrary, suggesting that the model captures much of the required computation while revealing imperfections in the learned kernel. This makes the framework useful both as a proof of principle for de
