Author

B. Cottereau

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Preprint Sep 2026

Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision

Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams naturally, but training deep SNNs with backpropagation through time (BPTT) requires substantial memory and remains difficult on neuromorphic hardware. Local learning avo...

M. Vaillant, Axel Carlier, Xing-Ng Lai et al. · 0 citations

Preprint Sep 2026

Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection

ChronoFuse is introduced, a causal availability-time detector that predicts object states for when its output becomes available rather than for when its input was observed, showing that predicting ahead can be critical for robots operating in fast-changing scenes.

Biswadeep Sen, B. Cottereau, N. Cuperlier et al. · 0 citations

#artificial intelligence Preprint Sep 2026

REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception

REACT is proposed, a fully spiking state-space model for event-driven temporal perception that processes raw events one by one, without temporal accumulation, and shows that event-driven spiking state-space dynamics can provide low-latency, continuously updated temporal perception for reactive robotic systems.

Geoffroy Keime, N. Cuperlier, B. Cottereau · 0 citations