Machine Learning Model Development Using Computational Neurology-Reference-Cited by-同舟云学术

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Machine Learning Model Development Using Computational Neurology

Published:2023 Issue: Volume: Page:149-158
ISSN:2190-3018
Container-title:Soft Computing and Signal Processing
language:
Short-container-title:

Author:

Kanrar Soumen

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8669-7_14

Reference8 articles.

1. Hahne, J., Dahmen, D., Schuecker, J., Formmer, A., Bolten, M., Helias, M., Diesmann, M.: Integration of continuous-time dynamics in a spiking neural network simulator. Front. Neuroinform. 11(34), 1–24 (2017). https://doi.org/10.3389/fninf.2017.00034

2. Hubel, H.D., Wiesel, N.T.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962). https://doi.org/10.1113/jphysiol.1962.sp006837

3. Rullen, V.R., Thorpe, J.S.: Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput. 13(6), 1255–1283 (2001). https://doi.org/10.1162/08997660152002852

4. Tavanaei, A., Ghodrati, M., Masquelier, T., Maida, A.: Deep learning in spiking neural networks, Neural Networks. Neural Netw. 111, 47–63 (2019) https://doi.org/10.1016/j.neunet.2018.12.002

5. Kistler, M.W., Gerstner, W., Hemmen, V.L.J.: Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Comput. 9(5), 1015–1045 (1997). https://doi.org/10.1162/neco.1997.9.5.1015

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