Real-time computation of a large-scaled entorhinal-hippocampal spiking neural network using GPU acceleration
Author:
Affiliation:
1. Department of Human Intelligence Systems, Kyushu Institute of Technology
2. Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine
Link
https://www.jstage.jst.go.jp/article/nolta/13/2/13_349/_pdf
Reference11 articles.
1. [1] K. Fujita, S. Okuno, and Y. Kashimori. “Evaluation of the computational efficacy in GPU-accelerated simulations of spiking neurons,” Computing, 2018. vol. 100, pp. 907-926, 2018.
2. [2] T. Yamazaki, and J. Igarashi. “Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit,” Neural Networks, vol. 47, pp. 103-111, 2013.
3. [3] J. O'Keefe, and J. Dostrovsky. “The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat,” Brain research, vol. 34, no. 1, pp. 171-175, 1971.
4. [4] T. Hafting, M. Fyhn, S. Molden, M. B. Moser, E. I. Moser. “Microstructure of a spatial map in the entorhinal cortex,” Nature, vol. 436, no. 7052, pp. 801-806, 2005. doi:10.1038/nature03721
5. [5] E. M. Izhikevich. “Simple Model of Spiking Neurons,” IEEE Transactions on Neural Networks, vol. 14, pp. 1569-1572, 2003.
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