{"id":52868,"date":"2024-09-26T20:07:29","date_gmt":"2024-09-26T12:07:29","guid":{"rendered":"https:\/\/swarma.org\/?p=52868"},"modified":"2024-09-26T20:07:29","modified_gmt":"2024-09-26T12:07:29","slug":"%e8%84%91%e7%a0%94%e7%a9%b6%e5%a6%82%e4%bd%95%e5%90%af%e5%8f%91%e8%b6%85%e4%bd%8e%e5%8a%9f%e8%80%97%e6%96%b0%e4%b8%80%e4%bb%a3ai%e6%a8%a1%e5%9e%8b%e5%92%8c%e7%ae%97%e6%b3%95%e7%9a%84%e8%ae%be%e8%ae%a1","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=52868","title":{"rendered":"\u8111\u7814\u7a76\u5982\u4f55\u542f\u53d1\u8d85\u4f4e\u529f\u8017\u65b0\u4e00\u4ee3AI\u6a21\u578b\u548c\u7b97\u6cd5\u7684\u8bbe\u8ba1\uff1f | \u5468\u516d\u76f4\u64ad\u00b7AI by Complexity\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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2. \u795e\u7ecf\u8109\u51b2\u653e\u7535\u6d3b\u52a8\u7684\u53d8\u5f02\u6027\u548c\u7a00\u758f\u6027\uff08S-CV\uff09\u53ca\u4e24\u8005\u7b49\u4ef7\u6027<\/span><\/p>\n

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    Huang, M., Lin, W., Roe, A., & Yu, Y. (2024) A Unified Theory of Response Sparsity and Variability for Energy-Efficient Neural Coding. Preprint on BIORXIV\/2024\/614987.<\/span><\/section>\n<\/li>\n
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    Rolls, E. T., & Tovee, M. J. (1995). Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Neurophysiology, 73, 713-726.<\/span><\/section>\n<\/li>\n
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    Treves, A., & Rolls, E. (2009). What determines the capacity of autoassociative memories in the brain? Network Computation in Neural Systems, 2, 371-397.<\/span><\/section>\n<\/li>\n
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    Haider, B., Krause, M. R., Duque, A., et al. (2010). Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation. Neuron, 65, 107-121.<\/span><\/section>\n<\/li>\n
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    Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14, 481-487.<\/span><\/section>\n<\/li>\n
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    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, 334-350.<\/span><\/section>\n<\/li>\n
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    Lengler, J., & Steger, A. (2017). Note on the coefficient of variations of neuronal spike trains. Biological Cybernetics, 111, 229-235.<\/span><\/section>\n<\/li>\n
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    Yu, Y., Migliore, M., Hines, M. L., & Shepherd, G. M. (2014). Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition. Journal of Neuroscience, 34, 13701\u201313713.<\/span><\/section>\n<\/li>\n
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    Yu, Y., McTavish, T. S., Hines, M. L., Shepherd, G. M., Valenti, C., & Migliore, M. (2013). Sparse distributed representation of odors in a large-scale olfactory bulb circuit. PLoS Computational Biology, 9, e1003014.<\/span><\/section>\n<\/li>\n
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    Marder, E. (2011). Variability, compensation, and modulation in neurons and circuits. Proceedings of the National Academy of Sciences, 108, 15542-15548.<\/span><\/section>\n<\/li>\n
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    Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9, 292-303.<\/span><\/section>\n<\/li>\n<\/ol>\n

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    \u5927\u6a21\u578b\u3001\u591a\u6a21\u6001\u3001\u591a\u667a\u80fd\u4f53\u5c42\u51fa\u4e0d\u7a77\uff0c\u5404\u79cd\u5404\u6837\u7684\u795e\u7ecf\u7f51\u7edc\u53d8\u4f53\u5728AI\u5927\u821e\u53f0\u5404\u663e\u8eab\u624b\u3002\u590d\u6742\u7cfb\u7edf\u9886\u57df\u5bf9\u4e8e\u6d8c\u73b0\u3001\u5c42\u7ea7\u3001\u9c81\u68d2\u6027\u3001\u975e\u7ebf\u6027\u3001\u6f14\u5316\u7b49\u95ee\u9898\u7684\u63a2\u7d22\u4e5f\u5728\u6301\u7eed\u63a8\u8fdb\u3002\u800c\u4f18\u79c0\u7684AI\u7cfb\u7edf\u3001\u521b\u65b0\u6027\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5f80\u5f80\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u5177\u5907\u4f18\u79c0\u590d\u6742\u7cfb\u7edf\u7684\u7279\u5f81\u3002\u56e0\u6b64\uff0c\u53d1\u5c55\u4e2d\u7684\u590d\u6742\u7cfb\u7edf\u7406\u8bba\u65b9\u6cd5\u5982\u4f55\u6307\u5bfc\u672a\u6765AI\u7684\u8bbe\u8ba1\uff0c\u6b63\u5728\u6210\u4e3a\u5907\u53d7\u5173\u6ce8\u7684\u95ee\u9898\u3002<\/span><\/p>\n


    <\/p>\n

    \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u52a0\u5229\u798f\u5c3c\u4e9a\u5927\u5b66\u5723\u8fed\u6208\u5206\u6821\u52a9\u7406\u6559\u6388\u5c24\u4ea6\u5e84\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u526f\u6559\u6388\u5218\u5b87\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u5728\u8bfb\u535a\u58eb\u5f20\u7ae0\u3001\u725f\u7267\u4e91\u548c\u5728\u8bfb\u7855\u58eb\u6768\u660e\u54f2\u3001\u6e05\u534e\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u7530\u6d0b\u5171\u540c\u53d1\u8d77\u300cAI By Complexity\u300d\u8bfb\u4e66\u4f1a<\/a>\uff0c\u63a2\u7a76\u5982\u4f55\u5ea6\u91cf\u590d\u6742\u7cfb\u7edf\u7684\u201c\u597d\u574f\u201d\uff1f\u5982\u4f55\u7406\u89e3\u590d\u6742\u7cfb\u7edf\u7684\u673a\u5236\uff1f\u8fd9\u4e9b\u7406\u89e3\u662f\u5426\u53ef\u4ee5\u542f\u53d1\u6211\u4eec\u8bbe\u8ba1\u66f4\u597d\u7684AI\u6a21\u578b\uff1f\u5728\u672c\u8d28\u4e0a\u5e2e\u52a9\u6211\u4eec\u8bbe\u8ba1\u66f4\u597d\u7684AI\u7cfb\u7edf\u3002\u8bfb\u4e66\u4f1a\u4e8e6\u670810\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u4e00\u665a\u4e0a20:00-22:00\u4e3e\u529e\u3002\u6b22\u8fce\u4ece\u4e8b\u76f8\u5173\u9886\u57df\u7814\u7a76\u3001\u5bf9AI+Complexity\u611f\u5174\u8da3\u7684\u670b\u53cb\u4eec\u62a5\u540d\u8bfb\u4e66\u4f1a\u4ea4\u6d41\uff01<\/span><\/p>\n


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

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


    <\/section>\n
    \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
    AI by Complexity \u8bfb\u4e66\u4f1a\u542f\u52a8\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a>
    <\/section>\n


    <\/p>\n


    <\/section>\n

    \u5f80\u671f\u5206\u4eab\uff1a<\/strong><\/span><\/p>\n

      \n
    1. \n
      \u7b2c\u4e00\u671f \u5f20\u7ae0 \u4e8e\u7389\u56fd \u7530\u6d0b \u725f\u7267\u4e91 \u5218\u5b87 \u6768\u660e\u54f2\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a><\/section>\n<\/li>\n
    2. \n
      \u7b2c\u4e8c\u671f \u5f90\u5955\u821f \u7fc1\u5eb7\u5b87\uff1a\u7edf\u8ba1\u7269\u7406\u4e0e\u4fe1\u606f\u8bba\u89c6\u89d2\u4e0b\uff0c\u7ed3\u6784\u5316\u566a\u58f0\u4e0e\u795e\u7ecf\u7f51\u7edc\u521d\u59cb\u5316\u7814\u7a76<\/span><\/a><\/section>\n<\/li>\n
    3. \n
      \u7b2c\u4e09\u671f \u5218\u5b87\uff1a\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\u4e0e\u7b97\u6cd5\u4fe1\u606f\u8bba<\/span><\/a><\/section>\n<\/li>\n
    4. \n
      \u7b2c\u56db\u671f \u7a0b\u5965\u534e \u718a\u5dcd\uff1a\u4ece\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u5230\u795e\u7ecf\u7b97\u5b50\u6a21\u578b\uff1a\u542f\u53d1\u66f4\u597d\u7684AI<\/span><\/a><\/section>\n<\/li>\n
    5. \n
      \u7b2c\u4e94\u671f \u848b\u6625\u6052\uff1a\u7f51\u7edc\u5c5e\u6027\u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u6027\u80fd<\/span><\/a><\/section>\n<\/li>\n
    6. \n
      \u7b2c\u516d\u671f \u6768\u660e\u54f2\uff1a\u56e0\u679c\u6d8c\u73b0\u542f\u53d1AI\u7684\u201c\u601d\u8003\u201d<\/span><\/a><\/section>\n<\/li>\n
    7. \n
      \u7b2c\u4e03\u671f \u6731\u7fa4\u559c\uff1a\u4ece\u590d\u6742\u7cfb\u7edf\u5230\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd<\/span><\/a><\/section>\n<\/li>\n
    8. \n

      \u7b2c\u4e5d\u671f \u5170\u5cb3\u6052\uff1a\u751f\u547d\u3001\u667a\u80fd\u6d8c\u73b0\u4e0e\u590d\u6742\u7cfb\u7edf\u7814\u7a76<\/span><\/a><\/p>\n<\/li>\n<\/ol>\n


      <\/section>\n


      <\/p>\n

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

      <\/mp-style-type><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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