ArticleFrontiers in neuroscience2026
A spiking continuous attractor network for event-driven neuromorphic robot guidance.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Event cameras provide fast, sparse visual measurements, but their output alone does not supply the persistent state required for closed-loop tracking. This study presents a hybrid event-driven perception-to-action system in which a Prophesee GenX320 (Prophesee SA, Paris, France) event camera and a lightweight Raspberry Pi 5 (Raspberry Pi Ltd., Cambridge, UK) front end provide target evidence to a spiking continuous attractor network running on a single SpiNNaker 2 (SpiNNcloud Systems GmbH, Dresden, Germany) chip. Recurrent difference-of-Gaussians connectivity forms a localised activity bump whose decoded position guides a simulated robot in a hardware-in-the-loop task. The network design is grounded in Amari's neural-field framework, with local excitation and broader inhibition used to select a compact, self-sustaining operating regime. On hardware, the measured transition between collapsed, stable, and saturated states follows the predicted stability corridor across 96 parameter settings. At the deployed operating point, the bump remains active for at least 8 s after input removal and tracks moving event-camera input across three target speeds. Against a registered target trajectory, the recurrent estimate has a mean error of 5.65 lattice cells and is 15% less jittery than the host-smoothed estimate that drives it. The median end-to-end latency from the close of an event-accumulation window to the corresponding bump update is 8.4 ms. In closed-loop guidance with periodic sensor dropout, all 10 recurrent and all 10 matched non-recurrent trials reached the goal, whereas the recurrent condition reached it 1.50 s sooner on average. A complementary finite-grid convergence analysis shows that weak, deployed, and stronger inhibition conditions exhibit expansion, bounded compact dynamics, and contraction, respectively. These results demonstrate that a continuum-guided, finite-grid-validated spiking attractor can provide persistent on-chip state for event-driven robot guidance.
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