Memristor-based adaptive leaky integrateand-fire neuron model: a simulation study

• Autorzy: Bednarz Karol , Garda Bartłomiej

Identyfikatory

DOI

10.15199/48.2025.03.05

Warianty tytułu

PL

Oparty na memrystorze model neuronu Adaptive Leaky Integrate-and-Fire: Badania symulacyjne

• Języki publikacji: EN

Abstrakty

EN

Due to the increasing demand for AI algorithms and the limitations of traditional von Neumann architectures, there is a need to develop new, more efficient architectures that operate in analogy to the human brain. In this study, an attempt was made to replicate the behaviour of a human neuron using memristor-based circuits. The correctness of the proposed solutions was verified through computer simulations.

PL

Ze względu na rosnące zapotrzebowanie na algorytmy sztucznej inteligencji oraz ograniczenia wynikające z tradycyjnych architektur von Neumanna, istnieje konieczność opracowania nowych, bardziej efektywnych architektur, które będą działać w analogii do funkcjonowania ludzkiego mózgu. W niniejszej pracy podjęto próbę odwzorowania zachowania ludzkiego neuronu, wykorzystując układy oparte na memrystorach. Poprawność zaproponowanych rozwiązań została zweryfikowana za pomocą symulacji komputerowych.

Słowa kluczowe

EN

memristor; neuromorphic computing; leaky integrate-and-fire neuron

PL

memrystor; obliczenia neuromorficzne; model neuronu LIF

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2025
• Tom: R. 101, nr 3
• Strony: 20--23
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Bednarz Karol

• Department of Electrical and Power Engineering Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering AGH University of Kraków al. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0009-0007-6857-4851

autor

Garda Bartłomiej

• Department of Electrical and Power Engineering Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering AGH University of Kraków al. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0001-6150-5617

Bibliografia

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• [12] Fang, X. et al. “Memristive LIF Spiking Neuron Model and Its Application in Morse Code”. Frontiers in Neuroscience, 16, Apr. 2022. ISSN: 1662-453X. DOI: 10.3389/fnins.2022.853010.
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