{"id":49651,"date":"2024-04-11T23:03:47","date_gmt":"2024-04-11T15:03:47","guid":{"rendered":"https:\/\/swarma.org\/?p=49651"},"modified":"2024-04-11T23:03:47","modified_gmt":"2024-04-11T15:03:47","slug":"%e4%bb%8e%e5%a4%8d%e6%9d%82%e7%a5%9e%e7%bb%8f%e5%8a%a8%e5%8a%9b%e5%ad%a6%e5%88%b0%e6%99%ba%e8%83%bd%e6%b6%8c%e7%8e%b0%ef%bc%9a%e5%9f%ba%e4%ba%8e%e7%a5%9e%e7%bb%8f%e5%a4%8d%e6%9d%82%e6%80%a7%e7%9a%84","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=49651","title":{"rendered":"\u4ece\u590d\u6742\u795e\u7ecf\u52a8\u529b\u5b66\u5230\u667a\u80fd\u6d8c\u73b0\uff1a\u57fa\u4e8e\u795e\u7ecf\u590d\u6742\u6027\u7684\u7c7b\u8111\u4eba\u5de5\u667a\u80fd"},"content":{"rendered":"
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2. \u5927\u8111\u53ef\u88ab\u8ba4\u4e3a\u662f\u8bad\u7ec3\/\u5b66\u4e60\u8fc7\u7684\u7f51\u7edc\uff1a\u590d\u6742\u7684\u7ed3\u6784-\u529f\u80fd-\u884c\u4e3a\u5173\u7cfb<\/span><\/strong><\/section>\n
3. \u673a\u5668\u5b66\u4e60 vs. \u795e\u7ecf\u79d1\u5b66<\/span><\/strong><\/section>\n
4. \u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7406\u89e3\u65f6\u95f4\u8ba1\u7b97<\/span><\/strong><\/section>\n
5. \u5c55\u671b\uff1a\u57fa\u4e8e\u795e\u7ecf\u590d\u6742\u6027\u7684\u7c7b\u8111\u4eba\u5de5\u667a\u80fd<\/strong><\/span><\/span><\/section>\n


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\u590d\u6742\u7684\u7ed3\u6784-\u529f\u80fd-\u884c\u4e3a\u5173\u7cfb<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u56fe4. \u5927\u8111\u4f5c\u4e3a\u52a8\u6001\u590d\u6742\u7cfb\u7edf<\/span><\/section>\n

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\u56fe5. \u8111\u7f51\u7edc\u542f\u53d1\u7684\u673a\u5668\u5b66\u4e60<\/span><\/section>\n

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\u795e\u7ecf\u79d1\u5b66\u4e0e\u4eba\u5de5\u667a\u80fd\u7684\u4ea4\u53c9\u7814\u7a76\uff0c\u63ed\u793a\u4e86\u5927\u8111\u7ed3\u6784\u3001\u52a8\u529b\u5b66\u548c\u529f\u80fd\u4e4b\u95f4\u590d\u6742\u7684\u5173\u7cfb\u3002\u5728\u7814\u7a76\u5927\u8111\u7ed3\u6784\u65f6\uff0c\u6211\u4eec\u5173\u6ce8\u795e\u7ecf\u5143\u4e4b\u95f4\u7684\u8fde\u63a5\u65b9\u5f0f\uff0c\u5305\u62ec\u6811\u7a81\u8f74\u7a81\u8fde\u63a5\u4ee5\u53ca\u4e0d\u540c\u8111\u533a\u4e4b\u95f4\u7684\u7f51\u7edc\u8fde\u63a5\u3002\u8fd9\u4e9b\u8fde\u63a5\u5448\u73b0\u51fa\u591a\u5c42\u6b21\u3001\u6a21\u5757\u5316\u7684\u7279\u70b9\uff0c\u63ed\u793a\u4e86\u5927\u8111\u5185\u90e8\u7684\u590d\u6742\u7ed3\u6784\u3002<\/span><\/section>\n

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Wang, R., Lin, P., Liu, M., Wu, Y., Zhou, T., & Zhou, C. (2019). Hierarchical connectome modes and critical state jointly maximize human brain functional diversity. Physical review letters, 123(3), 038301.<\/span><\/section>\n
Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021). Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proceedings of the National Academy of Sciences, 118(23), e2022288118.<\/span><\/section>\n<\/blockquote>\n

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\u56fe6. \u5927\u8111\u8fde\u63a5\u7ec4\u7684\u7ed3\u6784<\/span><\/section>\n

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\u795e\u7ecf\u52a8\u529b\u5b66\u65b9\u9762\u7684\u7814\u7a76\u53d1\u73b0\uff0c\u795e\u7ecf\u5143\u7684\u6d3b\u52a8\u5177\u6709\u81ea\u7ec4\u7ec7\u6d8c\u73b0\u7684\u7279\u5f81\uff0c\u8868\u73b0\u4e3a\u795e\u7ecf\u96ea\u5d29\u73b0\u8c61\u3002\u8fd9\u79cd\u73b0\u8c61\u5728\u4e0d\u540c\u5c3a\u5ea6\u4e0a\u90fd\u5b58\u5728\uff0c\u4ece\u5355\u4e2a\u7535\u6781\u5230\u6574\u4e2a\u5927\u8111\uff0c\u90fd\u53ef\u4ee5\u89c2\u5bdf\u5230\u795e\u7ecf\u5143\u7fa4\u4f53\u7684\u96c6\u7fa4\u53d1\u653e\u884c\u4e3a\uff0c\u8868\u73b0\u51fa\u4e00\u79cd\u4e34\u754c\u6d3b\u52a8\u72b6\u6001\u3002\u8fd9\u79cd\u72b6\u6001\u4f7f\u5f97\u5927\u8111\u5bf9\u5916\u754c\u523a\u6fc0\u975e\u5e38\u654f\u611f\uff0c\u80fd\u591f\u968f\u65f6\u505a\u51fa\u54cd\u5e94\uff0c\u5b9e\u73b0\u5404\u79cd\u529f\u80fd\uff0c\u800c\u4e14\u6d88\u8017\u80fd\u91cf\u8f83\u5c11<\/span>\uff08Friedman et al., 2012; Softky & Koch, 1993\uff09<\/span>\u3002<\/span><\/section>\n

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Friedman, N., Ito, S., Brinkman, B. A., Shimono, M., DeVille, R. L., Dahmen, K. A., . . . Butler, T. C. (2012). Universal critical dynamics in high resolution neuronal avalanche data. Physical review letters, 108(20), 208102.<\/span><\/section>\n
Softky, W. R., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of neuroscience, 13(1), 334-350.<\/span><\/section>\n<\/blockquote>\n

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Wang, R., Lin, P., Liu, M., Wu, Y., Zhou, T., & Zhou, C. (2019). Hierarchical connectome modes and critical state jointly maximize human brain functional diversity. Physical review letters, 123(3), 038301.<\/span><\/section>\n<\/blockquote>\n

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Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021). Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proceedings of the National Academy of Sciences, 118(23), e2022288118.<\/span><\/section>\n<\/blockquote>\n

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Chen, Y., Wang, S., Hilgetag, C. C., & Zhou, C. (2017). Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency. PLoS computational biology, 13(9), e1005776.<\/span><\/section>\n<\/blockquote>\n

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Liang, J., Wang, S.-J., & Zhou, C. (2022). Less is more: wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing. National Science Review, 9(3), nwab102.<\/span><\/section>\n<\/blockquote>\n

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Liang, J., Yang, Z., & Zhou, C. (2024). Excitation\u2013Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits. The Neuroscientist, 10738584231221766.<\/span><\/section>\n
\u76f8\u5173\u63a8\u6587\uff1a\u524d\u6cbf\u7efc\u8ff0\uff1a\u795e\u7ecf\u56de\u8def\u4e2d\u7684\u5174\u594b-\u6291\u5236\u5e73\u8861\u3001\u4e34\u754c\u6027\u4e0e\u795e\u7ecf\u6d3b\u52a8<\/a><\/span><\/section>\n<\/blockquote>\n

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\u5927\u8111\u4f5c\u4e3a\u4e00\u4e2a\u667a\u80fd\u7cfb\u7edf\uff0c\u542f\u53d1\u4e86\u8bb8\u591a\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u53d1\u5c55\u3002\u6700\u521d\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u5728\u4e0a\u4e2a\u4e16\u7eaa90\u5e74\u4ee3\u8fbe\u5230\u5dc5\u5cf0\uff0c\u4f46\u540e\u6765\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u7684\u5174\u8d77\uff0c\u7279\u522b\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u7684\u6210\u529f\uff0c\u518d\u6b21\u5f15\u53d1\u4e86AI\u9886\u57df\u7684\u70ed\u6f6e\u3002\u9664\u4e86\u89c6\u89c9\u7cfb\u7edf\uff0c\u8fd8\u6709\u5904\u7406\u8bed\u8a00\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5b83\u4eec\u7684\u8bbe\u8ba1\u4e0e\u5904\u7406\u4efb\u52a1\u5bc6\u5207\u76f8\u5173\u3002\u6c6a\u5c0f\u4eac\u8001\u5e08\u548c\u6768\u5149\u5b87\u7b49\u5b66\u8005\u5bf9\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u4e86\u6df1\u5165\u7684\u7814\u7a76\u548c\u4ecb\u7ecd\u3002<\/span><\/section>\n

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\n
Yang, G. R., & Wang, X.-J. (2020). Artificial neural networks for neuroscientists: a primer. Neuron, 107(6), 1048-1070.<\/span><\/section>\n<\/blockquote>\n

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\u5218\u5065\u8001\u5e08\u5219\u4e13\u6ce8\u4e8e\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u7814\u7a76\uff0c\u8fd9\u79cd\u7f51\u7edc\u4f7f\u7528\u8109\u51b2\u800c\u975e\u901f\u7387\u8fdb\u884c\u4fe1\u606f\u4f20\u9012\uff0c\u66f4\u63a5\u8fd1\u751f\u7269\u795e\u7ecf\u7cfb\u7edf\u7684\u5de5\u4f5c\u65b9\u5f0f\uff0c\u5e76\u5177\u6709\u4f4e\u80fd\u8017\u7684\u4f18\u52bf\u3002\u5f3a\u5316\u5b66\u4e60\u662f\u53e6\u4e00\u4e2a\u53d7\u5230\u5927\u8111\u542f\u53d1\u7684\u9886\u57df\uff0c\u5b83\u53ef\u4ee5\u5c06\u52a8\u7269\u5b9e\u9a8c\u4e2d\u7684\u5956\u8d4f\u673a\u5236\u8f6c\u5316\u4e3a\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u667a\u80fd\u7684\u5b66\u4e60\u7b56\u7565\u3002\u867d\u7136\u5df2\u6709\u8bb8\u591a\u76f8\u5173\u7814\u7a76\u548c\u5e94\u7528\uff0c\u4f46\u5bf9\u4e8e\u65b0\u8fdb\u5165\u8fd9\u4e2a\u9886\u57df\u7684\u4eba\u6765\u8bf4\uff0c\u4ecd\u9700\u6df1\u5165\u7814\u7a76\u73b0\u6709\u6587\u732e\uff0c\u5bfb\u627e\u524d\u6cbf\u9886\u57df\u5e76\u63a2\u7d22\u65b0\u7684\u7814\u7a76\u65b9\u5411\u3002\u56e0\u6b64\uff0c\u5bf9\u4e8e\u751f\u7269\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3001\u52a8\u529b\u5b66\u548c\u5b66\u4e60\u7b97\u6cd5\u7b49\u65b9\u9762\u7684\u6df1\u5165\u601d\u8003\u662f\u975e\u5e38\u6709\u610f\u4e49\u7684\u3002<\/span><\/section>\n

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\u5728\u795e\u7ecf\u79d1\u5b66\u9886\u57df\uff0c\u673a\u5668\u5b66\u4e60\u6280\u672f\u7684\u5e94\u7528\u6b63\u5728\u9010\u6e10\u6210\u4e3a\u7814\u7a76\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002\u795e\u7ecf\u79d1\u5b66\u7814\u7a76\u8005\u4eec\u9762\u5bf9\u7740\u795e\u7ecf\u7cfb\u7edf\u590d\u6742\u6027\u7684\u6311\u6218\uff0c\u5982\u4f55\u4ece\u4e2d\u63d0\u53d6\u7ed3\u6784\u548c\u52a8\u529b\u5b66\u4fe1\u606f\u6210\u4e3a\u4e86\u4e00\u4e2a\u91cd\u8981\u8bfe\u9898\u3002\u5927\u6570\u636e\u65f6\u4ee3\u7684\u5230\u6765\u4f7f\u5f97\u795e\u7ecf\u79d1\u5b66\u8fdb\u5165\u4e86\u4e00\u4e2a\u6570\u636e\u4e30\u5bcc\u7684\u65f6\u4ee3\uff0c\u8fd9\u4e5f\u4fc3\u4f7f\u7740\u7814\u7a76\u8005\u4eec\u5f00\u59cb\u601d\u8003\u5982\u4f55\u5229\u7528\u673a\u5668\u5b66\u4e60\u6280\u672f\u6765\u5904\u7406\u8fd9\u4e9b\u6d77\u91cf\u6570\u636e<\/strong>\u3002<\/span><\/section>\n

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\u4e3e\u4f8b\u6765\u8bf4\uff0c\u533b\u5b66\u9886\u57df\u7684\u7814\u7a76\u8005\u4eec\u5229\u7528MRI\u7b49\u5f71\u50cf\u6280\u672f\u8fdb\u884c\u80bf\u7624\u5206\u7c7b\u8bc6\u522b\uff0c\u5e76\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u6280\u672f\u6765\u8fdb\u884c\u8111\u533a\u5212\u5206\u548cmapping\u3002\u5728\u795e\u7ecf\u52a8\u529b\u5b66\u7814\u7a76\u4e2d\uff0c\u5bfb\u627e\u4e0e\u4efb\u52a1\u3001\u5e74\u9f84\u548c\u75be\u75c5\u76f8\u5173\u7684\u7279\u5f81\u6210\u4e3a\u4e86\u4e00\u9879\u91cd\u8981\u4efb\u52a1\uff0c\u8fd9\u4e9b\u7279\u5f81\u7684\u63d0\u53d6\u5e38\u5e38\u9700\u8981\u501f\u52a9\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u548c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u878d\u5408\u6df1\u5ea6\u5b66\u4e60\u548c\u5f71\u50cf\u5b66\u6280\u672f\uff0c\u53ef\u4ee5\u9884\u6d4b\u4e00\u4e2a\u4eba\u7684\u5e74\u9f84\uff0c\u8fd9\u79cd\u5e94\u7528\u5bf9\u533b\u5b66\u8bca\u65ad\u548c\u75be\u75c5\u76d1\u6d4b\u5177\u6709\u91cd\u8981\u610f\u4e49<\/span> (Cole & Franke, 2017)<\/span>\u3002\u6b64\u5916\uff0c\u56fe\u795e\u7ecf\u7f51\u7edc\u5728\u8ba4\u77e5\u7814\u7a76\u4e2d\u4e5f\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\uff0c\u5b83\u53ef\u4ee5\u5206\u6790\u8111\u7535\u4fe1\u53f7\u548c\u8111\u7ed3\u6784\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4e3a\u795e\u7ecf\u79d1\u5b66\u7814\u7a76\u63d0\u4f9b\u4e86\u65b0\u7684\u89c6\u89d2<\/span> (Klepl et al., 2024)<\/span>\u3002\u5c3d\u7ba1\u673a\u5668\u5b66\u4e60\u6280\u672f\u5728\u795e\u7ecf\u79d1\u5b66\u9886\u57df\u7684\u5e94\u7528\u6b63\u5728\u8fc5\u901f\u589e\u957f\uff0c\u4f46\u5176\u4e2d\u4ecd\u7136\u5b58\u5728\u7740\u4e00\u4e9b\u6311\u6218\uff0c\u5982\u7279\u5f81\u7684\u53ef\u89e3\u91ca\u6027\u7b49\u95ee\u9898\uff0c\u8fd9\u9700\u8981\u7ed3\u5408\u9886\u57df\u4e13\u4e1a\u77e5\u8bc6\u8fdb\u884c\u6df1\u5165\u7814\u7a76\u3002\u968f\u7740\u673a\u5668\u5b66\u4e60\u6280\u672f\u5728\u795e\u7ecf\u79d1\u5b66\u9886\u57df\u7684\u4e0d\u65ad\u53d1\u5c55\uff0c\u76f8\u4fe1\u672a\u6765\u5c06\u4f1a\u6709\u66f4\u591a\u7684\u521b\u65b0\u548c\u7a81\u7834\u3002<\/span><\/section>\n

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\n
Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends in neurosciences, 40(12), 681-690.<\/span><\/section>\n
Klepl, D., Wu, M., & He, F. (2024). Graph neural network-based eeg classification: A survey. IEEE Transactions on Neural Systems and Rehabilitation Engineering.<\/span><\/section>\n<\/blockquote>\n

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\u6700\u8fd1\uff0c\u4e00\u4e2a\u65b0\u5174\u4e14\u5f15\u4eba\u6ce8\u76ee\u7684\u65b9\u5411\u662f\u5c06\u673a\u5668\u5b66\u4e60\u5e94\u7528\u4e8e\u795e\u7ecf\u79d1\u5b66\u9886\u57df\u3002\u673a\u5668\u5b66\u4e60\u672c\u8eab\u6e90\u81ea\u4e8e\u5bf9\u4eba\u7c7b\u5927\u8111\u7684\u6a21\u4eff\uff0c\u4f46\u73b0\u5728\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06<\/strong>\u673a\u5668\u5b66\u4e60<\/strong>\u4f5c\u4e3a\u4e00\u79cd\u6a21\u578b\u6765\u7406\u89e3\u5927\u8111\u7684\u795e\u7ecf\u8868\u5f81\u548c\u8ba1\u7b97\u539f\u7406<\/strong>\u3002\u60f3\u8c61\u4e00\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u8f93\u5165\u7ed9\u5927\u8111\u7684\u56fe\u50cf\u8f93\u5165\u5230\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u4e2d\uff0c\u7136\u540e\u89c2\u5bdf\u5176\u7279\u5f81\u3002\u5c31\u50cf\u4eba\u7c7b\u63a5\u6536\u5916\u754c\u523a\u6fc0\u540e\u4ea7\u751f\u884c\u4e3a\u4e00\u6837\uff0c\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u4e5f\u53ef\u4ee5\u901a\u8fc7\u6a21\u62df\u7c7b\u4f3c\u7684\u4efb\u52a1\u8f93\u51fa\u7c7b\u4f3c\u7684\u884c\u4e3a\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u6a21\u62df\u5de5\u4f5c\u8bb0\u5fc6\u7684\u4efb\u52a1\uff0c\u6d4b\u8bd5\u5176\u5728\u8bc6\u522b\u989c\u8272\u548c\u4fdd\u6301\u989c\u8272\u4fe1\u606f\u65b9\u9762\u7684\u8868\u73b0\u3002<\/span><\/section>\n

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\u901a\u8fc7\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u7684\u8868\u5f81\uff0c\u6211\u4eec\u5e0c\u671b\u80fd\u591f\u7406\u89e3\u523a\u6fc0\u4e0e\u884c\u4e3a\u4e4b\u95f4\u7684\u6620\u5c04\u5173\u7cfb\uff0c\u4ece\u800c\u63ed\u793a\u5927\u8111\u8ba1\u7b97\u548c\u8868\u5f81\u7684\u539f\u7406\u3002\u8fd9\u79cd\u7814\u7a76\u65b9\u6cd5\u5bf9\u4e8e\u795e\u7ecf\u79d1\u5b66\u7684\u53d1\u5c55\u5177\u6709\u91cd\u8981\u610f\u4e49\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u903b\u8f91\u4e0a\u7684\u6846\u67b6\uff0c\u53ef\u4ee5\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u3002\u5c3d\u7ba1\u4e0d\u540c\u6a21\u578b\u7684\u5b9e\u73b0\u65b9\u5f0f\u53ef\u80fd\u4e0d\u540c\uff0c\u4f46\u5b83\u4eec\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u53ef\u80fd\u4f1a\u5171\u4eab\u76f8\u4f3c\u7684\u52a8\u529b\u5b66\u548c\u7ed3\u6784\u539f\u7406\u3002<\/span><\/section>\n

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\u56fe16. \u6211\u4eec\u53ef\u4ee5\u5c06\u673a\u5668\u5b66\u4e60\u4f5c\u4e3a\u6a21\u578b\u6765\u7406\u89e3\u5927\u8111\u7684\u795e\u7ecf\u8868\u5f81\u548c\u8ba1\u7b97\u539f\u7406<\/span><\/p>\n


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\u56fe17. \u5229\u7528\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u7814\u7a76\u5927\u8111<\/span><\/section>\n

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Kriegeskorte, N., & Kievit, R. A. (2013). Representational geometry: integrating cognition, computation, and the brain. Trends in cognitive sciences, 17(8), 401-412.<\/span><\/section>\n<\/blockquote>\n

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Devereux, B. J., Clarke, A., & Tyler, L. K. (2018). Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway. Scientific reports, 8(1), 10636.<\/span><\/section>\n<\/blockquote>\n

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Song, H. F., Yang, G. R., & Wang, X.-J. (2016). Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework. PLoS computational biology, 12(2), e1004792.<\/span><\/section>\n<\/blockquote>\n

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Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., … Cohen, A. (2022). Shared computational principles for language processing in humans and deep language models. Nature neuroscience, 25(3), 369-380.<\/span><\/section>\n<\/blockquote>\n

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Bi, Z., & Zhou, C. (2020). Understanding the computation of time using neural network models. Proceedings of the National Academy of Sciences, 117(19), 10530-10540.<\/span><\/section>\n<\/blockquote>\n

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\u63a8\u8350\u9605\u8bfb\u8bba\u6587\uff1a<\/strong><\/span><\/section>\n
[1] Joshua I.Glaser, Ari S.Benjamin, RoozbehFarhoodi, et al. The Roles of Supervised Machine Learning in Systems Neuroscience, 2019<\/span><\/section>\n
[2] Cole H.J., Franke K.. Predicting Aging Using Neuroimaging: Innovative Brain Ageing Biomarkers, 2017<\/span><\/section>\n
[3] Barry J. Devereux, Alex Clarke, Lorraine K. Tyler. Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway. scientific reports<\/em>, 2018, 8(1): 1-12<\/span><\/section>\n
[4] Yang G.R., Joglekar M.R., Song H.F.. Task representations in neural networks trained to perform many cognitive tasks. Nat Neurosci<\/em>, 2019, 22: 297\u2013306<\/span><\/section>\n
[5] Song H.F., Yang G.R., Wang X.J.. Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework, 2016, 12(2): 1004792<\/span><\/section>\n
[6] Guangyu Robert Yang, Xiao-Jing Wang. Artificial Neural Networks for Neuroscientists: A Primer. neuron<\/em>, 2020, 107(6): 1048-1070<\/span><\/section>\n
[7] Bi Z., Zhou C.. Understanding the computation of time using neural network models[J]. Proceedings of the National Academy of Sciences<\/em>, 2020, 117(19): 10530\u201310540<\/span><\/section>\n

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Bi, Z., & Zhou, C. (2020). Understanding the computation of time using neural network models. Proceedings of the National Academy of Sciences<\/em>, 117<\/em>(19), 10530-10540.<\/span><\/section>\n
Chen, Y., Wang, S., Hilgetag, C. C., & Zhou, C. (2017). Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency. PLoS computational biology<\/em>, 13<\/em>(9), e1005776.<\/span><\/section>\n
Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends in neurosciences<\/em>, 40<\/em>(12), 681-690.<\/span><\/section>\n
Devereux, B. J., Clarke, A., & Tyler, L. K. (2018). Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway. Scientific reports<\/em>, 8<\/em>(1), 10636.<\/span><\/section>\n
Friedman, N., Ito, S., Brinkman, B. A., Shimono, M., DeVille, R. L., Dahmen, K. A., . . . Butler, T. C. (2012). Universal critical dynamics in high resolution neuronal avalanche data. Physical review letters<\/em>, 108<\/em>(20), 208102.<\/span><\/section>\n
Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., . . . Cohen, A. (2022). Shared computational principles for language processing in humans and deep language models. Nature neuroscience<\/em>, 25<\/em>(3), 369-380.<\/span><\/section>\n
Klepl, D., Wu, M., & He, F. (2024). Graph neural network-based eeg classification: A survey. IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/em>.<\/span><\/section>\n
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Liang, J., Wang, S.-J., & Zhou, C. (2022). Less is more: wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing. National Science Review<\/em>, 9<\/em>(3), nwab102.<\/span><\/section>\n
Liang, J., Yang, Z., & Zhou, C. (2024). Excitation\u2013Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits. The Neuroscientist<\/em>, 10738584231221766.<\/span><\/section>\n
Softky, W. R., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of neuroscience<\/em>, 13<\/em>(1), 334-350.<\/span><\/section>\n
Song, H. F., Yang, G. R., & Wang, X.-J. (2016). Training excitatory-inhibitory recurrent neural networks for cognitive tasks: a simple and flexible framework. PLoS computational biology<\/em>, 12<\/em>(2), e1004792.<\/span><\/section>\n
Wang, R., Lin, P., Liu, M., Wu, Y., Zhou, T., & Zhou, C. (2019). Hierarchical connectome modes and critical state jointly maximize human brain functional diversity. Physical review letters<\/em>, 123<\/em>(3), 038301.<\/span><\/section>\n
Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021). Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proceedings of the National Academy of Sciences<\/em>, 118<\/em>(23), e2022288118.<\/span><\/section>\n
Yang, G. R., & Wang, X.-J. (2020). Artificial neural networks for neuroscientists: a primer. Neuron<\/em>, 107<\/em>(6), 1048-1070. <\/span><\/section>\n


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\u6591\u56fe\u5730\u5740\uff1ahttps:\/\/pattern.swarma.org\/study_group_issue\/602<\/span><\/section>\n

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1. <\/strong>\u524d\u6cbf\u7efc\u8ff0\uff1a\u795e\u7ecf\u56de\u8def\u4e2d\u7684\u5174\u594b-\u6291\u5236\u5e73\u8861\u3001\u4e34\u754c\u6027\u4e0e\u795e\u7ecf\u6d3b\u52a8<\/a><\/strong><\/span><\/section>\n
2. <\/strong>\u590d\u6742\u6027\u4e2d\u4f55\u4ee5\u6d8c\u73b0\u7b80\u5355\u6027\uff1fSloppy\u6a21\u578b\u6355\u6349\u590d\u6742\u7cfb\u7edf\u7684\u5173\u952e\u81ea\u7531\u5ea6<\/a><\/strong><\/span><\/section>\n
3. <\/strong>\u795e\u7ecf\u590d\u6742\u6027\u7ecf\u5178\u7efc\u8ff0\uff1a\u6d8c\u73b0\u7684\u590d\u6742\u795e\u7ecf\u52a8\u529b\u5b66<\/a><\/strong><\/span>
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4. <\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
5.<\/strong> \u9f99\u5e74\u5927\u8fd0\u8d77\uff0c\u5b66\u4e60\u6b63\u5f53\u65f6\uff01\u89e3\u9501\u96c6\u667a\u5168\u7ad9\u5185\u5bb9\uff0c\u5f00\u542f\u65b0\u5e74\u5b66\u4e60\u8ba1\u5212<\/a><\/strong><\/strong><\/strong><\/strong><\/span><\/section>\n
6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n


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\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/strong><\/span><\/strong><\/span><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n

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