Brain Science and Brain-inspired Artificial Intelligence: Advances and Trends

Lidong Wang^1, and Cheryl Ann Alexander²

¹Institute for Systems Engineering Research, Mississippi State University, Vicksburg, Mississippi, USA

²Institute for IT innovation and Smart Health, Vicksburg, Mississippi, USA

Cite this paper:
Lidong Wang and Cheryl Ann Alexander. Brain Science and Brain-inspired Artificial Intelligence: Advances and Trends. Journal of Computer Sciences and Applications. 2019; 7(1):56-61. doi: 10.12691/jcsa-7-1-9

Abstract

Brain science and brain-inspired artificial intelligence have been very significant areas. They have a wide range of applications including military and defense, intelligent manufacturing, business intelligence and management, medical service and healthcare, etc. Many countries have launched national brain-related projects to increase the national interests and capability in the competitive global world. In this paper, we introduce some concepts, principles, and emerging technologies of brain science and brain-inspired artificial intelligence; present their advances and trends; and outline some challenges in brain-inspired computing and computation based on spiking-neural-networks (SNNs). Specifically, the advances and trends cover brain-inspired computing, neuromorphic computing systems, and multi-scale brain simulation, brain association graph, brainnetome and the connectome, brain imaging, brain-inspired chips and brain-inspired devices, brain-computer interface (BCI) and brain-machine interface (BMI), brain-inspired robotics and applications, quantum robots, and cyborg (human-machine hybrids).

Keywords:
brain science brain-inspired artificial intelligence brain-inspired computing brain association graph brainnetome brain imaging brain-inspired chip brain-computer interface brain-inspired robot cyborg

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References:

[1]	Kang WM, Kim CH, Lee S, Woo SY, Bae JH, Park BG, Lee JH. A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices. In2019 International Joint Conference on Neural Networks (IJCNN) 2019 Jul 14 (pp. 1-7). IEEE.
[2]	Liu Y, Zheng FB. Object-oriented and multi-scale target classification and recognition based on hierarchical ensemble learning. Computers & Electrical Engineering. 2017 Aug 1; 62: 538-54.
[3]	Eördegh G, Őze A, Bodosi B, Puszta A, Pertich Á, Rosu A, Godó G, Nagy A. Multisensory guided associative learning in healthy humans. PloS one. 2019 Mar 12;14(3):e0213094.
[4]	Koelmans WW, Sebastian A, Jonnalagadda VP, Krebs D, Dellmann L, Eleftheriou E. Projected phase-change memory devices. Nature communications. 2015 Sep 3; 6: 8181.
[5]	Jin H, Hou LJ, Wang ZG. Military Brain Science–How to influence future wars. Chinese Journal of Traumatology. 2018 Oct 1; 21(5): 277-80.
[6]	Ielmini D. Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks. Microelectronic Engineering. 2018 Apr 15; 190: 44-53.
[7]	Sun Y, Xu H, Liu S, Song B, Liu H, Liu Q, Li Q. Short-term and long-term plasticity mimicked in low-voltage Ag/GeSe/TiN electronic synapse. IEEE Electron Device Letters. 2018 Feb 26; 39(4): 492-5.
[8]	Montagna F, Rahimi A, Benatti S, Rossi D, Benini L. PULP-HD: Accelerating brain-inspired high-dimensional computing on a parallel ultra-low power platform. InProceedings of the 55th Annual Design Automation Conference 2018 Jun 24 (p. 111). ACM.
[9]	Shamsi J, Shokouhi SB, Mohammadi K. On the capacity of Columnar Organized Memory (COM). In2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) 2018 Aug 5 (pp. 65-68). IEEE.
[10]	Zhong N, Yau SS, Ma J, Shimojo S, Just M, Hu B, Wang G, Oiwa K, Anzai Y. Brain informatics-based big data and the wisdom web of things. IEEE Intelligent Systems. 2015 Sep 4; 30(5): 2-7.
[11]	Hasan MS, Schuman CD, Najem JS, Weiss R, Skuda ND, Belianinov A, Collier CP, Sarles SA, Rose GS. Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network. In 2018 IEEE 13th Dallas Circuits and Systems Conference (DCAS) 2018 Nov 12 (pp. 1-6). IEEE.
[12]	Indiveri G, Liu SC. Memory and information processing in neuromorphic systems. Proceedings of the IEEE. 2015 Jul 15; 103(8): 1379-97.
[13]	Lu K, Li Y, He WF, Chen J, Zhou YX, Duan N, Jin MM, Gu W, Xue KH, Sun HJ, Miao XS. Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing. Applied Physics A. 2018 Jun 1; 124(6): 438.
[14]	Amunts K, Ebell C, Muller J, Telefont M, Knoll A, Lippert T. The human brain project: creating a European research infrastructure to decode the human brain. Neuron. 2016 Nov 2; 92(3): 574-81.
[15]	Yin D, Chen X, Zeljic K, Zhan Y, Shen X, Yan G, Wang Z. A graph representation of functional diversity of brain regions. Brain and behavior. 2019 Sep 1.
[16]	Ho TC, Dennis EL, Thompson PM, Gotlib IH. Network-based approaches to examining stress in the adolescent brain. Neurobiology of stress. 2018 Feb 1; 8: 147-57.
[17]	Kopetzky S, Butz-Ostendorf M. From matrices to knowledge: Using semantic networks to annotate the connectome. Frontiers in neuroanatomy. 2018; 12: 111.
[18]	Tadić B, Andjelković M, Melnik R. functional Geometry of Human connectomes. Scientific reports. 2019 Aug 19; 9(1): 1-2.
[19]	Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M. Fundamental activity constraints lead to specific interpretations of the connectome. PLoS computational biology. 2017 Feb 1; 13(2): e1005179.
[20]	Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. NeuroImage. 2019 Jun 12.
[21]	Nazempour R, Liu C, Chen Y, Ma C, Sheng X. Performance evaluation of an implantable sensor for deep brain imaging: an analytical investigation. Optical Materials Express. 2019 Sep 1; 9(9): 3729-37.
[22]	Song M, Zhang Y, Cui Y, Yang Y, Jiang T. Brain network studies in chronic disorders of consciousness: advances and perspectives. Neuroscience bulletin. 2018 Aug 1;34(4):592-604.
[23]	Song C, Liu B, Liu C, Li H, Chen Y. Design techniques of eNVM-enabled neuromorphic computing systems. In2016 IEEE 34th International Conference on Computer Design (ICCD) 2016 Oct 2 (pp. 674-677). IEEE.
[24]	Sayyaparaju S, Amer S, Rose GS. A bi-memristor synapse with spike-timing-dependent plasticity for on-chip learning in memristive neuromorphic systems. In2018 19th International Symposium on Quality Electronic Design (ISQED) 2018 Mar 13 (pp. 69-74). IEEE.
[25]	Chakraborty I, Saha G, Sengupta A, Roy K. Toward fast neural computing using all-photonic phase change spiking neurons. Scientific reports. 2018 Aug 28; 8(1): 12980.
[26]	Kim D, Byun W, Ku Y, Kim JH. High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces. IEEE Access. 2019 Apr 24; 7: 55169-79.
[27]	Kim GH, Kim K, Lee E, An T, Choi W, Lim G, Shin JH. Recent progress on microelectrodes in neural interfaces. Materials. 2018 Oct; 11(10): 1995.
[28]	Bing Z, Meschede C, Röhrbein F, Huang K, Knoll AC. A survey of robotics control based on learning-inspired spiking neural networks. Frontiers in neurorobotics. 2018 Jul 6; 12: 35.
[29]	Li J, Li Z, Chen F, Bicchi A, Sun Y, Fukuda T. Combined Sensing, Cognition, Learning and Control to Developing Future Neuro-Robotics Systems: A Survey. IEEE Transactions on Cognitive and Developmental Systems. 2019 Feb 5.
[30]	Wei F, Yin C, Zheng J, Zhan Z, Yao L. Rise of cyborg microrobot: different story for different configuration. IET nanobiotechnology. 2019 Jun 6; 13(7): 651-64.
[31]	Gonçalves CP. Quantum Robotics, Neural Networks and the Quantum Force Interpretation. Neural Networks and the Quantum Force Interpretation (September 5, 2018). 2018 Sep 5.
[32]	Reinares-Lara E, Olarte-Pascual C, Pelegrín-Borondo J. Do you want to be a cyborg? The moderating effect of ethics on neural implant acceptance. Computers in Human Behavior. 2018 Aug 1; 85: 43-53.