Abstract
This paper describes neural network algorithms and software that scale up to massively parallel computers. The neuron model used is the best available at this time, the Hodgkin-Huxley equations. Most massively parallel simulations use very simplified neuron models, which cannot accurately simulate biological neurons and the wide variety of neuron types. Using C++ and MPI we can scale these networks to human-level sizes. Computers such as the Chinese TianHe computer are capable of human level neural networks.
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References
Skocik, M.J., Long, L.N.: On the capabilities and computational costs of neuron models. IEEE Trans. Neural Netw. Learn. 25(8), 1474–1483 (2014)
Long, L.N.: Efficient neural network simulations using the Hodgkin-Huxley equations. In: Conference on 60 Years of Hodgkin and Huxley, Trinity College, Cambridge, 12–13 July 2012
Markram, H., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492 (2015)
Bower, J.M., David, B.: The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Springer, Heidelberg (2012)
Reimann, M.W., Anastassiou, C.A., Perin, R., Hill, S.L., Markram, H., Koch, C.: Biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79, 375–390 (2013)
Kelley, T.D., Avery, E., Long, L.N., Dimperio, E.: A hybrid symbolic and sub-symbolic intelligent system for mobile robots. In: InfoTech@Aerospace Conference, Seattle, WA, AIAA, Reston, VA, AIAA Paper 2009–1976 (2009)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. J. Physiol. 117, 500–544 (1952)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)
Bostrom, N.: SuperIntelligence: Paths, Dangers, Strategies, Oxford Press, Oxford, England (2014)
Long, L.N., Gupta, A., Fang, G.: A computational approach to neurogenesis and synaptogenesis using biologically plausible models with learning. In: Frontiers in Systems Neuroscience, Conference Abstract: Computational and Systems Neuroscience (COSYNE) Meeting, Salt Lake City, Utah, Feb. 25–28 2010
French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128–135 (1999)
Long, L.N., Gupta, A.: Scalable massively parallel artifical neural networks. J. Aerosp. Comput. Inf. Commun. (JACIC), 5(1), 3–15 (2008)
Gupta, A., Long, L.N.: Hebbian learning with winner take all for spiking neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), Atlanta, Georgia, June 14–19 (2009)
Long, L.N.: An adaptive spiking neural network with hebbian learning. In: Presented at the IEEE Workshop on Evolving and Adaptive Intelligent Systems, Symposium Series on Computational Intelligence, Paris, France, April 11–15 (2011)
Long, L.N.: Scalable biologically inspired neural networks with spike time based learning. In: Invited Paper, IEEE Symposium on Learning and Adaptive Behavior in Robotic Systems, Edinburgh, Scotland, Aug. 6–8 2008
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Long, L.N. (2016). Toward Human-Level Massively-Parallel Neural Networks with Hodgkin-Huxley Neurons. In: Steunebrink, B., Wang, P., Goertzel, B. (eds) Artificial General Intelligence. AGI 2016. Lecture Notes in Computer Science(), vol 9782. Springer, Cham. https://doi.org/10.1007/978-3-319-41649-6_32
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DOI: https://doi.org/10.1007/978-3-319-41649-6_32
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