{"id":51243,"date":"2024-07-09T17:27:22","date_gmt":"2024-07-09T09:27:22","guid":{"rendered":"https:\/\/swarma.org\/?p=51243"},"modified":"2024-07-09T17:27:22","modified_gmt":"2024-07-09T09:27:22","slug":"%e5%9f%ba%e4%ba%8e%e7%9c%bc%e5%8a%a8%e4%bf%a1%e5%8f%b7%e8%a7%a3%e7%a0%81%e7%9a%84%e8%a7%86%e8%a7%89%e9%87%8d%e5%bb%ba%e4%b8%a8%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad%c2%b7%e8%84%91%e6%9c%ba%e6%8e%a5","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51243","title":{"rendered":"\u57fa\u4e8e\u773c\u52a8\u4fe1\u53f7\u89e3\u7801\u7684\u89c6\u89c9\u91cd\u5efa\u4e28\u5468\u65e5\u76f4\u64ad\u00b7\u8111\u673a\u63a5\u53e3\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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    \n
  1. \n
    Johnston, Ren\u00e9e, et al. “Decoding saccade intention from primate prefrontal cortical local field potentials using spectral, spatial, and temporal dimensionality reduction.” International Journal of Neural Systems<\/em> 31.06 (2021): 2150023.<\/span><\/section>\n<\/li>\n
  2. \n
    Graf, Arnulf BA, and Richard A. Andersen. “Brain\u2013machine interface for eye movements.” Proceedings of the National Academy of Sciences<\/em> 111.49 (2014): 17630-17635.<\/span><\/section>\n<\/li>\n
  3. \n
    Angjelichinoski, Marko, et al. “Cross-subject decoding of eye movement goals from local field potentials.” Journal of neural engineering<\/em> 17.1 (2020): 016067.<\/span><\/section>\n<\/li>\n
  4. \n
    Glaser, Joshua I., et al. “Machine learning for neural decoding.” Eneuro<\/em> 7.4 (2020).<\/span><\/section>\n<\/li>\n
  5. \n

    Ahmadi, Nur, Timothy G. Constandinou, and Christos-Savvas Bouganis. “Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.” Journal of Neural Engineering<\/em> 18.2 (2021): 026011.<\/span><\/p>\n<\/li>\n<\/ol>\n

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    \u8bfb\u4e66\u4f1a\u6210\u5458\u5747\u53ef\u4ee5\u5728\u8bfb\u4e66\u4f1a\u671f\u95f4\u7533\u8bf7\u6210\u4e3a\u4e3b\u8bb2\u4eba\u3002\u4e3b\u8bb2\u4eba\u4f5c\u4e3a\u8bfb\u4e66\u4f1a\u6210\u5458\uff0c\u5747\u9075\u5faa\u5185\u5bb9\u5171\u521b\u5171\u4eab\u673a\u5236\uff0c\u53ef\u4ee5\u83b7\u5f97\u62a5\u540d\u8d39\u9000\u6b3e\uff0c\u5e76\u5171\u4eab\u672c\u8bfb\u4e66\u4f1a\u4ea7\u751f\u7684\u6240\u6709\u5185\u5bb9\u8d44\u6e90\u3002\u5177\u4f53\u89c1\u7cfb\u5217\u8bfb\u4e66\u4f1a\u8be6\u60c5\uff1a\u4ece\u79d1\u5e7b\u5230\u73b0\u5b9e\uff0c\u4ece\u53d1\u73b0\u5230\u5e94\u7528\uff1a\u8111\u673a\u63a5\u53e3\u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span><\/section>\n

    <\/section>\n
    \n

    <\/section>\n
    \n
    \n
    \n
    \n

    \u8111\u673a\u63a5\u53e3\u8bfb\u4e66\u4f1a\u62db\u52df\u4e2d<\/strong>
    <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u8111\u673a\u63a5\u53e3\u662f\u901a\u8fc7\u8bfb\u53d6\u5927\u8111\u795e\u7ecf\u4fe1\u53f7\u6765\u5b9e\u73b0\u4eba\u8111\u4e0e\u5916\u90e8\u8bbe\u5907\u4ea4\u6d41\u4e0e\u63a7\u5236\u7684\u524d\u6cbf\u6280\u672f\u3002\u4f5c\u4e3a\u4e00\u4e2a\u524d\u6cbf\u4ea4\u53c9\u9886\u57df\uff0c\u8111\u673a\u63a5\u53e3\u6280\u672f\u662f\u8de8\u5b66\u79d1\u7814\u7a76\u7684\u5178\u578b\u4ee3\u8868\uff0c\u878d\u5408\u4e86\u63a7\u5236\u79d1\u5b66\u3001\u795e\u7ecf\u79d1\u5b66\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u3001\u5de5\u7a0b\u5b66\u7b49\u591a\u4e2a\u5b66\u79d1\u9886\u57df\u3002\u9488\u5bf9\u76f8\u5173\u9886\u57df\uff0c\u96c6\u667a\u5df2\u7ecf\u4e3e\u529e\u4e86\u591a\u4e2a\u7cfb\u5217\u8bfb\u4e66\u4f1a\u4e0e\u8bfe\u7a0b\uff0c\u8ffd\u8e2a\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u3001NeuroAI\u3001\u795e\u7ecf\u52a8\u529b\u5b66\u6a21\u578b\u3001\u63a7\u5236\u79d1\u5b66\u7b49\u8fdb\u5c55\u3002<\/span><\/p>\n


    <\/span><\/p>\n

    \u4e3a\u4e86\u8fdb\u4e00\u6b65\u68b3\u7406\u8111\u673a\u63a5\u53e3\u76f8\u5173\u7406\u8bba\u4e0e\u6280\u672f\u524d\u6cbf\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u6e05\u534e\u5927\u5b66\u9ad8\u5c0f\u6995\u3001\u4e2d\u79d1\u9662\u81ea\u52a8\u5316\u6240\u5218\u51b0\u3001\u4e2d\u79d1\u9662\u6df1\u5733\u5148\u8fdb\u9662\u674e\u9a81\u5065\u3001\u6e05\u534e\u5927\u5b66\u772d\u4e9a\u6960\u56db\u4f4d\u8001\u5e08\uff0c\u53d1\u8d77\u300c\u8111\u673a\u63a5\u53e3\u300d\u8bfb\u4e66\u4f1a\u3002\u8bfb\u4e66\u4f1a\u4ece2024\u5e745\u670818\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u516d19:00-21:00\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba18-10 \u5468\uff0c\u6b22\u8fce\u4ece\u4e8b\u76f8\u5173\u7814\u7a76\u3001\u5bf9\u8111\u673a\u63a5\u53e3\u611f\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\u4ea4\u6d41\u3002<\/span><\/p>\n<\/section>\n

    \n

    <\/span><\/strong><\/span><\/section>\n

    <\/span><\/span><\/strong><\/span><\/a><\/p>\n


    <\/span><\/strong><\/span><\/section>\n
    \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
    \u4ece\u79d1\u5e7b\u5230\u73b0\u5b9e\uff0c\u4ece\u53d1\u73b0\u5230\u5e94\u7528\uff1a\u8111\u673a\u63a5\u53e3\u8bfb\u4e66\u4f1a\u542f\u52a8<\/span><\/a><\/section>\n
    \n

    <\/section>\n<\/section>\n<\/section>\n


    <\/p>\n

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