2024/10/22 by Bernhard Moser, Moser, Bernhard A., Michael Lunglmayr +1
Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Optical Imaging Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.17441
openalex publication_date 2024/10/22 · openalex created_date 2024/11/13 · openalex updated_date 2026/07/28
In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily triggered by a change in the perceived stimulus. We show in a rigorous mathematical way that information encoding by means of Threshold-Based Representation based on either Leaky Integrate-and-Fire (LIF) or Send-on-Delta (SOD) is linked to an analog-to-spike conversion that guarantees maximum sparsity while satisfying an approximation condition based on the Alexiewicz norm.