Arshia Eslami

Challenging the Spatiotemporal Processing of Neuromorphic Models through a Temporally-Rich Event-Based Dataset

The research rigorously investigates how neuromorphic architectures encode and integrate temporal information by conducting a comprehensive ablation study using a hybrid network, and demonstrates that shallow neuromorphic integration effectively maximizes the gains from temporal integration while mitigating the information loss inherent in binary spike quantization.