3 papers
cs.NE2025
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks
Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran +1
Spiking Neural Networks (SNNs) with their bio-inspired Leaky Integrate-and-Fire (LIF) neurons inherently capture temporal information. This makes them well-suited for sequential ta…
cs.NE2025
TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy, Charlotte Frenkel
The demand for low-power inference and training of deep neural networks (DNNs) on edge devices has intensified the need for algorithms that are both scalable and energy-efficient.…
cs.NE2024
LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Marco Paul E. Apolinario, Arani Roy, Kaushik Roy
Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-dev…