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cs.CV2026
Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization
Ruichen Ma, Xiaoyang Zhang, Jian Bai +5
The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce…
cs.CV2025
I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
Ruichen Ma, Liwei Meng, Guanchao Qiao +3
Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This work introduces I2E, an a…