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<p>Spiking neural networks (SNNs) are inspired from natural computing, modelling with high accuracy the interactions and processes between the synapses of the neurons focusing on low response time and energy efficiency. This novel paradigm of event-based processing opens new opportunities for discovering applications and developing efficient learning methods that should highlight the advantages of SNNs such as the large memory capacity and the fast adaptation, while preserving the easy-to-use and portability of the conventional computing architectures. In this paper, we do a brief review of the developments of the past decades in the field of SNNs. We start with a brief history of the SNN and summarize the most common models of spiking neurons and methods to implement synaptic plasticity. We also classify the SNNs according to the implemented learning rules and network topology. We present the computational advantages, liabilities, and applications suitable for using SNNs in terms of energy efficiency and response time. In addition, we briefly sweep through the existing platforms and simulation frameworks for SNNs exploration. The paper ends with conclusions that show predictions of future challenges and the emerging research topics associated with SNNs.</p>
</abstract>

Spiking neural networks (SNNs) are inspired from natural computing, modelling with high accuracy the interactions and processes between the synapses of the neurons focusing on low response time and energy efficiency. This novel paradigm of event-based processing opens new opportunities for discovering applications and developing efficient learning methods that should highlight the advantages of SNNs such as the large memory capacity and the fast adaptation, while preserving the easy-to-use and portability of the conventional computing architectures. In this paper, we do a brief review of the developments of the past decades in the field of SNNs. We start with a brief history of the SNN and summarize the most common models of spiking neurons and methods to implement synaptic plasticity. We also classify the SNNs according to the implemented learning rules and network topology. We present the computational advantages, liabilities, and applications suitable for using SNNs in terms of energy efficiency and response time. In addition, we briefly sweep through the existing platforms and simulation frameworks for SNNs exploration. The paper ends with conclusions that show predictions of future challenges and the emerging research topics associated with SNNs.

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

“Gheorghe Asachi” Technical University of Iași

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Spiking neural networks (SNNs) are inspired from natural computing, modelling with high accuracy the interactions and processes between the synapses of the...

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

Online veröffentlicht: 08. Mai 2023

Seitenbereich: 81 - 98

Eingereicht: 09. Nov. 2022

Akzeptiert: 17. März 2023

DOI: https://doi.org/10.2478/bipie-2022-0012

Schlüsselwörter
spiking neural network, neurorobotics, neuromorphic hardware, Hebbian learning, survey

© 2022 George-Iulian Uleru et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

Online veröffentlicht: 08. Mai 2023

Seitenbereich: 81 - 98

Eingereicht: 09. Nov. 2022

Akzeptiert: 17. März 2023

DOI: https://doi.org/10.2478/bipie-2022-0012

Schlüsselwörterspiking neural network, neurorobotics, neuromorphic hardware, Hebbian learning, survey

© 2022 George-Iulian Uleru et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
spiking neural network, neurorobotics, neuromorphic hardware, Hebbian learning, survey