Robustness Analysis of Adaptive Neural Network Model Based on Spike Timing-Dependent Plasticity_Journal of Biomedical Engineering

Authors：

CHENYunzhi ,  XUGuizhi , ZHOUQian , GUOMiaomiao , GUOLei , WANXiaowei

Province of State Key Laboratory Cultivation Base Construction, Hebei University of Technology, Tianjin 300401, China;

Corresponding author：

XUGuizhi, Email: gzxu@hebut.edu.cn

Keywords：

electromagnetic bionic protection; neural network; spike timing-dependent plasticity; adaptive robust capacity

DOI：

10.7507/1001-5515.20150005

Video：

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To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.

Citation： CHENYunzhi, XUGuizhi, ZHOUQian, GUOMiaomiao, GUOLei, WANXiaowei. Robustness Analysis of Adaptive Neural Network Model Based on Spike Timing-Dependent Plasticity. Journal of Biomedical Engineering, 2015, 32(1): 25-31. doi: 10.7507/1001-5515.20150005 Copy

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15.	IZHIKEVICH E M. Which model to use for cortical spiking neurons?[J]. IEEE Trans Neural Netw, 2004, 15(5): 1063-1070.
16.	林凌鹏, 林水生, 刘培龙, 等.STDP突触的双输入信号动力学模型研究[J].计算机应用研究, 2012, 29(4):1311-1313.
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1. 巨政权, 张海川, 满梦华.一种适用于功能电路的自组织神经网络模型构建[J].电讯技术, 2011, 51(8):90-95.
2. 刘尚合, 原亮, 褚杰.电磁仿生学-电磁防护研究的新领域[J].自然杂志, 2009, 31(1):1-7.
3. MARKRAM H, LVBKE J, FROTSCHER M, et al. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs[J]. Science, 1997, 275(5297): 213-215.
4. DAN Y, POO M M. Spike timing-dependent plasticity: from synapse to perception[J]. Physiol Rev, 2006, 86(3): 1033-1048.
5. HEBB D O. The organization of behavior:a neuropsychological theory[M]. New York: Psychology Press, 1949.
6. VAN ROSSUM M C, BI G Q, TURRIGIANO G G. Stable Hebbian learning from spike timing-dependent plasticity[J]. J Neurosci, 2000, 20(23): 8812-8821.
7. KUBOTA S, KITAJIMA T. How balance between LTP and LTD can be controlled in spike-timing-dependent learning rule[C]//International Joint Conference on Neural Networks, Atlanta, GA:2009:1674-1679.
8. HONDA M, URAKUBO H, KOUMURA T, et al. A common framework of signal processing in the induction of cerebellar LTD and cortical STDP[J]. Neural Netw, 2013, 43: 114-124.
9. SONG S, MILLER K D, ABBOTT L F. Competitive hebbian learning through spike-timing-dependent synaptic plasticity[J]. Nat Neurosci, 2000, 3(9): 919-926.
10. QIN Y M, WANG J, MEN C, et al. Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields[J]. China Phys B, 2012, 21(7), 078702: 1-7.
11. SONG S, ABBOTT L F. Cortical development and remapping through spike Timing-Dependent plasticity[J]. Neuron, 2001, 32(2): 339-350.
12. TAKAHASHI Y K, KORI H, MASUDA N. Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2009, 79(5 Pt 1): 051904.
13. MORRISON A, AERTSEN A, DIESMANN M. Spike-timing-dependent plasticity in balanced random networks[J]. Neural Comput, 2007, 19(6): 1437-1467.
14. SOLTOGGIO A, STANLEY K O. From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation[J]. Neural Netw, 2012, 34: 28-41.
15. IZHIKEVICH E M. Which model to use for cortical spiking neurons?[J]. IEEE Trans Neural Netw, 2004, 15(5): 1063-1070.
16. 林凌鹏, 林水生, 刘培龙, 等.STDP突触的双输入信号动力学模型研究[J].计算机应用研究, 2012, 29(4):1311-1313.
17. SCARPETTA S, GIACCO F, LOMBARDI F, et al. Effects of poisson noise in a IF model with STDP and spontaneous replay of periodic spatiotemporal patterns, in absence of cue stimulation[J]. Biosystems, 2013, 112(3): 258-264.
18. FERNANDO S, YAMADA K. Spike-Timing-dependent plasticity and short-term plasticity jointly control the excitation of hebbian plasticity without weight constraints in neural networks[J]. Comput Intell Neurosci, 2012, 2012: 968272.
19. GHANI A, MCDAID L, BELATRECHE A, et al. Evaluating the generalisation capability of a CMOS based synapse[J]. Neurocomputing, 2012, 83: 188-197.

Journal of Biomedical Engineering

Robustness Analysis of Adaptive Neural Network Model Based on Spike Timing-Dependent Plasticity

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