Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks
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 architectural choices best accommodate local and online learning in multi-layer convolutional SNNs. This question is addressed via an original framework combining residual-like connections with multi-depth feature aggregation and consensus. The full SNN pipeline features an early-vision front end, to convert raw visual data into sparse spike latencies, a four-layer convolutional backbone trained layerwise with unsupervised spike-timing-dependent plasticity (STDP), a deterministic Multi-Depth Temporal Fusion (MDTF) an...
| Authors | Aidin Attar, Eleonora Cicciarella, Michele Rossi |
|---|---|
| Primary category | cs.NE |
| Published | 29 September 2026 |
| Last updated at source | 30 September 2026 |
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