Current - Issue
Original Article
Neuromorphic Communication Systems: A Brain-Inspired Approach for Future Networks
Dr.T. Elavarasi1
S. Rohithkanna2
S. Muralidharan3
K. Theerthan4
1 2 3 4 Department of computer science and Applications, Jeppiaar college of arts and science, Chennai, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 457-465
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502052
References
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https://ieeexplore.ieee.org/document/6809149
2. P. A. Merolla, et al., “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science, 2014.
https://doi.org/10.1126/science.1254642
3. M. Davies, et al., “Loihi: A neuromorphic manycore processor with on-chip learning,” IEEE Micro, 2018 https://doi.org/10.1109/
MM.2018.112130359
4. S. Furber, et al., “The SpiNNaker project,” Proceedings of the IEEE, 2014. https://doi.org/10.1109/JPROC.2014.2304638
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postsynaptic cell type,” Journal of Neuroscience, 1998. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998
6. F. Zenke, et al., “A biophysically-based neuromorphic model of spike-rate and timing-dependent plasticity,” PNAS, 2011.
https://doi.org/10.1073/pnas.1106161108
7. M. R. Azghadi, S. Al-Sarawi, D. Abbott, and N. Iannella, “A neuromorphic VLSI design for spike timing and rate-based synaptic plasticity,”
Neural Networks, vol. 45, pp. 70–82, 2013. https://doi.org/10.1016/j.neunet.2013.03.003
8. N. Skatchkovsky, H. Jang, and O. Simeone, “Spiking Neural Networks—Part III: Neuromorphic Communications,” IEEE Communications
Letters, vol. 25, no. 6, pp. 1746–1750, 2021. https://doi.org/10.1109/LCOMM.2021.3050212
9. N. Skatchkovsky, H. Jang, and O. Simeone, “Spiking Neural Networks—Part III: Neuromorphic Communications,” (preprint pdf). [10] W.
Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002.
10. https://doi.org/10.1017/CBO9780511815706
11. D. O. Hebb, The Organization of Behavior: ANeuropsychological
Theory.https://books.google.com/books/about/The_Organization_of_Behavior.html?id=1IhqAAAAMAAJ
12. Skatchkovsky, N., Jang, H., & Simeone, O. (2020). SNNs for Low-Power Remote Inference with Impulse Radio. arXiv preprint.
https://arxiv.org/abs/2010.14220
13. Linares-Barranco, B. & Serrano-Gotarredona, T. (2009). Memristance can explain Spike-Time-Dependent
PlasticityinNeuralSynapses.NatPrec.doi:10.1038/npre.2009.3010.1 https://www.nature.com/articles/npre.2009.3010.1?utm_
14. MDPI Sensors (2023). Neuromorphic Sentiment Analysis Using Spiking Neural Networks. Sensors, 23(18):7701.
doi:10.3390/s23187701https://www.mdpi.com/1424 8220/23/18/7701?utm_
15. Nowotny, T., Zhigulin, V. P., Selverston, A. I., Abarbanel, H. D., & Rabinovich, M. I. (2003). Enhancement of Synchronization in a Hybrid
Neural Circuit by Spike-Timing Dependent Plasticity. J. Neurosci., 23(30):9776–9785. doi:10.1523/JNEUROSCI.23-30-09776.2003
https://pmc.ncbi.nlm.nih.gov/articles/PMC6740898/
16. Balaji, A., Das, A., Wu, Y., Huynh, K., Dell’Anna, F., Indiveri, G., Krichmar, J. L., Dutt, N. D., Schaafsma, S., & Catthoor, F. (2019).
Mapping Spiking Neural Networks to Neuromorphic Hardware. arXiv preprint. Available at: https://arxiv.org/abs/1909.01843 arxiv.org
17. Huynh, P. K., Varshika, M. L., Paul, A., Isik, M., Balaji, A., & Das, A. (2022). Implementing Spiking Neural Networks on Neuromorphic
Architectures: A Review. arXiv preprint. Available at: https://arxiv.org/abs/2202.08897 arxiv.org
https://ieeexplore.ieee.org/document/6809149
2. P. A. Merolla, et al., “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science, 2014.
https://doi.org/10.1126/science.1254642
3. M. Davies, et al., “Loihi: A neuromorphic manycore processor with on-chip learning,” IEEE Micro, 2018 https://doi.org/10.1109/
MM.2018.112130359
4. S. Furber, et al., “The SpiNNaker project,” Proceedings of the IEEE, 2014. https://doi.org/10.1109/JPROC.2014.2304638
5. G. Q. Bi and M. M. Poo, “Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and
postsynaptic cell type,” Journal of Neuroscience, 1998. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998
6. F. Zenke, et al., “A biophysically-based neuromorphic model of spike-rate and timing-dependent plasticity,” PNAS, 2011.
https://doi.org/10.1073/pnas.1106161108
7. M. R. Azghadi, S. Al-Sarawi, D. Abbott, and N. Iannella, “A neuromorphic VLSI design for spike timing and rate-based synaptic plasticity,”
Neural Networks, vol. 45, pp. 70–82, 2013. https://doi.org/10.1016/j.neunet.2013.03.003
8. N. Skatchkovsky, H. Jang, and O. Simeone, “Spiking Neural Networks—Part III: Neuromorphic Communications,” IEEE Communications
Letters, vol. 25, no. 6, pp. 1746–1750, 2021. https://doi.org/10.1109/LCOMM.2021.3050212
9. N. Skatchkovsky, H. Jang, and O. Simeone, “Spiking Neural Networks—Part III: Neuromorphic Communications,” (preprint pdf). [10] W.
Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002.
10. https://doi.org/10.1017/CBO9780511815706
11. D. O. Hebb, The Organization of Behavior: ANeuropsychological
Theory.https://books.google.com/books/about/The_Organization_of_Behavior.html?id=1IhqAAAAMAAJ
12. Skatchkovsky, N., Jang, H., & Simeone, O. (2020). SNNs for Low-Power Remote Inference with Impulse Radio. arXiv preprint.
https://arxiv.org/abs/2010.14220
13. Linares-Barranco, B. & Serrano-Gotarredona, T. (2009). Memristance can explain Spike-Time-Dependent
PlasticityinNeuralSynapses.NatPrec.doi:10.1038/npre.2009.3010.1 https://www.nature.com/articles/npre.2009.3010.1?utm_
14. MDPI Sensors (2023). Neuromorphic Sentiment Analysis Using Spiking Neural Networks. Sensors, 23(18):7701.
doi:10.3390/s23187701https://www.mdpi.com/1424 8220/23/18/7701?utm_
15. Nowotny, T., Zhigulin, V. P., Selverston, A. I., Abarbanel, H. D., & Rabinovich, M. I. (2003). Enhancement of Synchronization in a Hybrid
Neural Circuit by Spike-Timing Dependent Plasticity. J. Neurosci., 23(30):9776–9785. doi:10.1523/JNEUROSCI.23-30-09776.2003
https://pmc.ncbi.nlm.nih.gov/articles/PMC6740898/
16. Balaji, A., Das, A., Wu, Y., Huynh, K., Dell’Anna, F., Indiveri, G., Krichmar, J. L., Dutt, N. D., Schaafsma, S., & Catthoor, F. (2019).
Mapping Spiking Neural Networks to Neuromorphic Hardware. arXiv preprint. Available at: https://arxiv.org/abs/1909.01843 arxiv.org
17. Huynh, P. K., Varshika, M. L., Paul, A., Isik, M., Balaji, A., & Das, A. (2022). Implementing Spiking Neural Networks on Neuromorphic
Architectures: A Review. arXiv preprint. Available at: https://arxiv.org/abs/2202.08897 arxiv.org
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