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一种大数据新算法———脉冲神经网络(SNN)

2017-01-07 新煮意的世界 新煮意的世界

本文是对脉冲神经网络(Spiking Neural Networks,SNNs)的一篇基础性综述,阐述了SNN网络的一些基本原则、概念和模型,以及存在的技术难点和挑战,对于大数据和算法研究者而言,是一篇较好的总结性文章,对于兴趣爱好者而言也是一篇不错的了解文章。 简述: 在过去的近十年内,随着计算神经科学中的脉冲神经元模型的不断受人关注,多种不同的脉冲神经网络模型也随之不断涌现。本文对这些几近

本文是对脉冲神经网络(Spiking Neural Networks,SNNs)的一篇基础性综述,阐述了SNN网络的一些基本原则、概念和模型,以及存在的技术难点和挑战,对于大数据和算法研究者而言,是一篇较好的总结性文章,对于兴趣爱好者而言也是一篇不错的了解文章。 简述: 在过去的近十年内,随着计算神经科学中的脉冲神经元模型的不断受人关注,多种不同的脉冲神经网络模型也随之不断涌现。本文对这些几近完美的SNN网络的背景以及遇到的挑战做了一个提纲性的介绍。 脉冲神经元模型: 图一 脉冲神经元:真实的生物神经元通过脉冲-尖峰(pulses - spikes)序列进行信息传输。 上图中,(a)一个神经元的突触树、轴突和细胞体;(b)上部分:从其它神经元得到的输入脉冲从突触向突触后的神经元传递;下部分:模型的简化图。(c)神经细胞的膜电势随着输入脉冲信号额变化,当膜电势超过阈值后会被重置到一个较低的水平,同时会产生一个脉冲(spike)。 脉冲信号的传输和处理过程如图一所示:动作电位首先通过轴突并激活突触,这些被激活的突触所释放的神经递质会快速到达突触后神经元。突触后的神经元的膜电势受到神经递质的

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    2018-11-13 124601c1m08暂无昵称

    这也好意思叫综述

    0

  2. [GetPortalCommentsPageByObjectIdResponse(id=352475, encodeId=986e3524e56d, content=这也好意思叫综述, beContent=null, objectType=article, channel=null, level=null, likeNumber=81, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=f17c2482611, createdName=124601c1m08暂无昵称, createdTime=Tue Nov 13 16:41:34 CST 2018, time=2018-11-13, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=198033, encodeId=84c419803376, content=没看明白,收藏后慢慢看。, beContent=null, objectType=article, channel=null, level=null, likeNumber=90, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=00501987275, createdName=130****4638, createdTime=Sun May 14 15:20:18 CST 2017, time=2017-05-14, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1943186, encodeId=9f4f194318647, content=<a href='/topic/show?id=0abe585885c' target=_blank style='color:#2F92EE;'>#新算法#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=68, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=58588, encryptionId=0abe585885c, topicName=新算法)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=373b263, createdName=lisa438, createdTime=Sun Nov 19 11:24:00 CST 2017, time=2017-11-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=170527, encodeId=90a61e052722, content=学习了,收藏了, beContent=null, objectType=article, channel=null, level=null, likeNumber=88, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/mONcle9pic3zMoyicyo6ia9f4IuLQAwZoxD6Hx4ibd5CMcOCYhStY6oDibbKK6O2X8iaicldO5ib8j1iapOIobIKCGiczU2A/0, createdBy=56251941490, createdName=虈亣靌, createdTime=Sun Jan 15 12:23:39 CST 2017, time=2017-01-15, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1630343, encodeId=6c0d16303430f, content=<a href='/topic/show?id=44e0e449217' target=_blank style='color:#2F92EE;'>#神经网络#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=52, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74492, encryptionId=44e0e449217, topicName=神经网络)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=e6ff21497204, createdName=by2016, createdTime=Sun Jan 08 15:24:00 CST 2017, time=2017-01-08, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168588, encodeId=bbfe1685888c, content=这是交叉学科吗?, beContent=null, objectType=article, channel=null, level=null, likeNumber=91, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/NUyjXTCJjo6Nx0VGA4QMDzOP8oAgnV4h51EjSfehJ9QxyiaSmw8PB56BhY8KXFSxHCibXrzcy4abrzSibXQPxJ9kzrLcJyhhB8a/0, createdBy=1d3b1672603, createdName=明月清辉, createdTime=Sat Jan 07 12:59:30 CST 2017, time=2017-01-07, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168557, encodeId=39ac16855ee2, content=不错,很好, beContent=null, objectType=article, channel=null, level=null, likeNumber=99, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/NUyjXTCJjo7LIiaRZTzJ3SEfiauCzTMPW7YvPPYLNJBXG9oh6Al1icq2VQQkIHWxqXehcicTS62YKJVBhxeth3wggw/0, createdBy=7ce01621306, createdName=天涯183, createdTime=Sat Jan 07 11:42:55 CST 2017, time=2017-01-07, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168481, encodeId=4195168481bb, content=用来算什么的?, beContent=null, objectType=article, channel=null, level=null, likeNumber=59, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=aadd1680584, createdName=蔬菜, createdTime=Sat Jan 07 08:22:36 CST 2017, time=2017-01-07, status=1, ipAttribution=)]
    2017-05-14 130****4638

    没看明白,收藏后慢慢看。

    0

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    2017-11-19 lisa438
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    2017-01-15 虈亣靌

    学习了,收藏了

    0

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  6. [GetPortalCommentsPageByObjectIdResponse(id=352475, encodeId=986e3524e56d, content=这也好意思叫综述, beContent=null, objectType=article, channel=null, level=null, likeNumber=81, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=f17c2482611, createdName=124601c1m08暂无昵称, createdTime=Tue Nov 13 16:41:34 CST 2018, time=2018-11-13, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=198033, encodeId=84c419803376, content=没看明白,收藏后慢慢看。, beContent=null, objectType=article, channel=null, level=null, likeNumber=90, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=00501987275, createdName=130****4638, createdTime=Sun May 14 15:20:18 CST 2017, time=2017-05-14, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1943186, encodeId=9f4f194318647, content=<a href='/topic/show?id=0abe585885c' target=_blank style='color:#2F92EE;'>#新算法#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=68, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=58588, encryptionId=0abe585885c, topicName=新算法)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=373b263, createdName=lisa438, createdTime=Sun Nov 19 11:24:00 CST 2017, time=2017-11-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=170527, encodeId=90a61e052722, content=学习了,收藏了, beContent=null, objectType=article, channel=null, level=null, likeNumber=88, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/mONcle9pic3zMoyicyo6ia9f4IuLQAwZoxD6Hx4ibd5CMcOCYhStY6oDibbKK6O2X8iaicldO5ib8j1iapOIobIKCGiczU2A/0, createdBy=56251941490, createdName=虈亣靌, createdTime=Sun Jan 15 12:23:39 CST 2017, time=2017-01-15, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1630343, encodeId=6c0d16303430f, content=<a href='/topic/show?id=44e0e449217' target=_blank style='color:#2F92EE;'>#神经网络#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=52, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74492, encryptionId=44e0e449217, topicName=神经网络)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=e6ff21497204, createdName=by2016, createdTime=Sun Jan 08 15:24:00 CST 2017, time=2017-01-08, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168588, encodeId=bbfe1685888c, content=这是交叉学科吗?, beContent=null, objectType=article, channel=null, level=null, likeNumber=91, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/NUyjXTCJjo6Nx0VGA4QMDzOP8oAgnV4h51EjSfehJ9QxyiaSmw8PB56BhY8KXFSxHCibXrzcy4abrzSibXQPxJ9kzrLcJyhhB8a/0, createdBy=1d3b1672603, createdName=明月清辉, createdTime=Sat Jan 07 12:59:30 CST 2017, time=2017-01-07, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168557, encodeId=39ac16855ee2, content=不错,很好, beContent=null, objectType=article, channel=null, level=null, likeNumber=99, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/NUyjXTCJjo7LIiaRZTzJ3SEfiauCzTMPW7YvPPYLNJBXG9oh6Al1icq2VQQkIHWxqXehcicTS62YKJVBhxeth3wggw/0, createdBy=7ce01621306, createdName=天涯183, createdTime=Sat Jan 07 11:42:55 CST 2017, time=2017-01-07, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=168481, encodeId=4195168481bb, content=用来算什么的?, beContent=null, objectType=article, channel=null, level=null, likeNumber=59, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=aadd1680584, createdName=蔬菜, createdTime=Sat Jan 07 08:22:36 CST 2017, time=2017-01-07, status=1, ipAttribution=)]
    2017-01-07 明月清辉

    这是交叉学科吗?

    0

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    2017-01-07 天涯183

    不错,很好

    0

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    2017-01-07 蔬菜

    用来算什么的?

    0

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