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cs.NE2026
Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks
Aidin Attar, Eleonora Cicciarella, Michele Rossi
We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key research question is which archi…
cs.NE2026
Combining Convolution and Delay Learning in Recurrent Spiking Neural Networks
Lúcio Folly Sanches Zebendo, Eleonora Cicciarella, Michele Rossi
Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we cont…
cs.NE2026
Sparse Spike Encoding of Channel Responses for Energy Efficient Human Activity Recognition
Eleonora Cicciarella, Riccardo Mazzieri, Jacopo Pegoraro +1
ISAC enables pervasive monitoring, but modern sensing algorithms are often too complex for energy-constrained edge devices. This motivates the development of learning techniques th…