Work

Optimal approximate computation of Euclidean distance in spiking neural P systems framework

Year: 2025

Type: article

Abstract: A fast approximation for the Euclidean distance, i.e., the [Formula: see text]-norm of a complex number was investigated. The problem is obtaining an initial estimate with minimal effort and maximum numerical precision. We show how to eliminate the square root computation and improve the precision of an approximation with just a few shifts and additions from 41% to just 4%. This corresponds to an improvement of precision from 1 bit to almost 5 bits. As successive Newton iterations double the number of bits, this eliminates the need for two initial iterations. For example, a result with 16-bit precision can be obtained by just two iterations instead of four. We implement the computation by using two types of basic neurons, namely a high-pass (HP) neuron and a low-pass (LP) neuron, in the Spiking Neural P systems framework.

Source: Scientific Reports

DOI: 10.1038/s41598-025-02793-3

Authors: Otgonbayar Agvaan, Gordon Cichon, Uuganbaatar Dulamragchaa, Hyun-chul Kim, Seonuck Paek, Tseren-Onolt Ishdorj

Institutions: Mongolian University of Science and Technology, Mongolian Academy of Sciences, Sangmyung University

Language: English

Topic: DNA and Biological Computing

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Korea International Cooperation Agency