NEUROMORPHIC COMPUTING – SPIKE NEURAL NETWORKS
DOI:
https://doi.org/10.24867/30BE45PopovicKeywords:
Neuromorphic computing, Spiking Neural Networks, LIF modelAbstract
Neuromorphic computing is an evolving field with increasing practical applications. This paper will briefly present the principles of neuromorphic computing, as well as the results of implementation a Spiking Neural Network, where the synapses are trained to adjust their weights according to the corresponding inputs.
References
[1] W. Gerstner, W.M. Kistler, “Spiking neuron models: Single neurons, populations, plasticity”, New York, US: Cambridge University Press, 2002. isbn: 978-0-521-81384-6. doi: 10.1017/CBO9780511815706.
[2] A.L. Hodgkin, A.F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol., Vol. 117, no. 4, pp. 500–544, Aug. 1952. [Online]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1392413/
[3] E. Izhikevich, “Simple model of spiking neurons”, IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1569–1572, Nov. 2003. doi: 10.1109/TNN.2003.820440.
[4] N. Caporale, Y. Dan, “Spike timing-dependent plasticity: a Hebbian learning rule,” eng, Annual Review of Neuroscience, Vol. 31, pp. 25–46, 2008. doi: 10.1146/ annurev.neuro.31.060407.125639.
[2] A.L. Hodgkin, A.F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol., Vol. 117, no. 4, pp. 500–544, Aug. 1952. [Online]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1392413/
[3] E. Izhikevich, “Simple model of spiking neurons”, IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1569–1572, Nov. 2003. doi: 10.1109/TNN.2003.820440.
[4] N. Caporale, Y. Dan, “Spike timing-dependent plasticity: a Hebbian learning rule,” eng, Annual Review of Neuroscience, Vol. 31, pp. 25–46, 2008. doi: 10.1146/ annurev.neuro.31.060407.125639.
Downloads
Published
2025-04-04
Issue
Section
Electrotechnical and Computer Engineering
