Integrating predictive coding with reservoir computing in spiking neural network model of cultured neurons
Author:
Affiliation:
1. Future University Hakodate
2. Oita University
3. Tohoku University
4. International Research Center for Neurointelligence (IRCN)
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/nolta/15/2/15_432/_pdf
Reference15 articles.
1. [1] Y. Katori, “Network Model for Dynamics of Perception with Reservoir Computing and Predictive Coding,” Advances in Cognitive Neurodynamics (VI), pp. 89-95, June 2018. DOI: 10.1007/978-981-10-8854-4_11
2. [2] R.P.N. Rao and D.H. Ballard, “Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects,” Nature Neuroscience, vol. 2, no. 1, pp. 79-87, January 1999. DOI: 10.1038/4580
3. [3] G. Tanaka, T. Yamane, J.B. Heroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks, vol. 115, pp. 100-123, July 2019. DOI: 10.1016/j.neunet.2019.03.005
4. [4] K.P. Dockendorf, I. Park, P. He, J.C. Principe, and T.B. DeMarse, “Liquid state machines and cultured cortical networks: The separation property,” Biosystems, vol. 95, no. 2, pp. 90-97, February 2009. DOI: 10.1007/978-981-10-8854-4_11
5. [5] T. Sumi, H. Yamamoto, Y. Katori, K. Ito, S. Moriya, T. Konno, S. Sato, and A. Hirano-Iwata, “Biological neurons act as generalization filters in reservoir computing,” Proc. Natl. Acad. Sci. U.S.A., vol. 120, no. 25, e2217008120, June 2023. DOI: 10.1073/pnas.2217008120
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