Spiking Neural Networks (SNNs): A Path towards Brain-Inspired AI
DOI:
https://doi.org/10.4314/0hd1n838Keywords:
Spiking Neural Networks. Brain-Inspired AI, Neuromorphic Computing,.Event-Driven Processing. Edge ComputingAbstract
Spiking Neural Networks (SNNs) represent a significant step toward brain-inspired artificial intelligence by mimicking the temporal dynamics and energy efficiency of biological neurons. Unlike traditional artificial neural networks, SNNs process information through discrete spikes, enabling event-driven computation and efficient learning mechanisms. This paradigm shift enhances real-time processing, low-power consumption, and neuromorphic computing applications. With advancements in hardware and training algorithms, SNNs hold great promise for edge computing, robotics, and cognitive modelling. This paper explores the fundamental principles of SNNs, their advantages over conventional deep learning models, and the challenges in developing large-scale, efficient spiking architectures.
Downloads
Published
Issue
Section
How to Cite
Most read articles by the same author(s)
- Enefiok Archibong Etuk, Omankwu, Obinnaya Chinecherem Beloved, Human-AI Collaboration: Enhancing Decision-Making in Critical Sectors , Communication In Physical Sciences: Vol. 12 No. 2 (2025): VOLUME 12 ISSUE 2
Similar Articles
- Chinyan Blessing, Sharon Oluwaseun, Taiwo Ruth Owoeye, Arti Raikwar, Beyond Automation: Data-Driven Financial Process Optimization and Organizational Transformation in Financial Services , Communication In Physical Sciences: Vol. 12 No. 8 (2025): VOLUME 12 ISSUE 8
- Moses Oluwasegun Odewale, Moses Olagoke Odejobi, Olanrewaju Oluwaseun Ajayi, Machine Learning-Based Predictive Congestion Management and Dynamic Resource Allocation in 5G Networks. , Communication In Physical Sciences: Vol. 9 No. 4 (2023): VOLUME 9 ISSUE 4
- Olatunde Ayeomoni, Enhancing Data Provenance, Integrity, Security, and Trustworthiness in Distributed and Federated Multi-Cloud Computing Environments , Communication In Physical Sciences: Vol. 11 No. 4 (2024): VOLUME 11 ISSUE 4
- Oluwatosin Lawal, Projecting AI-Driven Intersection of FinTech, Financial Compliance, and Technology Law , Communication In Physical Sciences: Vol. 12 No. 2 (2025): VOLUME 12 ISSUE 2
- Olatunde Ayeomoni, Sugar Raymond, Jude Okwuchukwu Ogene, Assessing Transparency and Accountability Mechanisms in AI-Driven Audit Tools , Communication In Physical Sciences: Vol. 11 No. 4 (2024): VOLUME 11 ISSUE 4
- Simbiat Atinuke Lawal, Samuel Omefe, Adeseun Kafayat Balogun, Comfort Michael, Sakiru Folarin Bello, Itunu Taiwo Owen, Kevin Nnaemeka Ifiora, Circular Supply Chains in the Al Era with Renewable Energy Integration and Smart Transport Networks , Communication In Physical Sciences: Vol. 7 No. 4 (2021): VOLUME 7 ISSUE 4
- Adebayo Adegbenro, Arinze Madueke, Aniedi Ojo, Cynthia Alabi, AI-Driven Wealth Advisory: Machine Learning Models for Personalized Investment Portfolios and Risk Optimization , Communication In Physical Sciences: Vol. 8 No. 4 (2022): VOLUME 8 ISSUE 4
- David Adetunji Ademilua, Edoise Areghan, AI-Driven Cloud Security Frameworks: Techniques, Challenges, and Lessons from Case Studies , Communication In Physical Sciences: Vol. 8 No. 4 (2022): VOLUME 8 ISSUE 4
- Confidence Ifeoma Odoh, Nweze Rosemary Chika Nweze, Ukamaka Victoria Maduahonwu, Development of an Enhanced Predictive Maintenance Models for Industrial Systems using Deep Learning Techniques , Communication In Physical Sciences: Vol. 13 No. 1 (2026): VOLUME 13 ISSUE 1
- Akintunde Stephen Samakinde , Vincent Bailey Arohunmolase, A Review of Machine Learning-Based Geochemical Signature Analysis for Mineral Prospectivity Mapping. , Communication In Physical Sciences: Vol. 13 No. 1 (2026): VOLUME 13 ISSUE 1
You may also start an advanced similarity search for this article.