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@article{204542,
        author = {Divya V and Pravitha V Devan},
        title = {Neuromorphic Digital and Hybrid Circuits for Spiking Neural Networks: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {224-228},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204542},
        abstract = {Neuromorphic computing represents a paradigm shift in hardware design, moving away from traditional von Neumann architectures to mimic the brain's efficient information processing. This review examines three primary architectural approaches for Spiking Neural Networks (SNNs): fully digital designs, the massively parallel Spinnaker system, and hybrid digital-analog circuits. Digital architectures like True North and Loihi prioritize scalability and programmability, utilizing CMOS technology and asynchronous networks-on-chip to handle large-scale neural simulations. SpiNNaker specifically addresses real-time biological modeling through a unique packet-based interconnect architecture.},
        keywords = {Neuromorphic computing, Spiking Neural Networks, Digital circuits, Hybrid circuits, SpiNNake.},
        month = {June},
        }