Work

On the sampling sparsity of analog-to-spike conversion based on leaky integrate-and-fire

Year: 2025

Type: article

Abstract: 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 or Send-on-Delta is linked to an analog-to-spike conversion that guarantees maximum sparsity while satisfying an approximation condition based on the Alexiewicz norm.

Source: Neuromorphic Computing and Engineering

DOI: 10.1088/2634-4386/adb884

Authors: Bernhard A Moser, Michael Lunglmayr

Institution: Johannes Kepler University of Linz

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,835

Funders: Silicon Austria Labs, HORIZON EUROPE Framework Programme