[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f6J8Cb66T7Fe6jvkEHdPpd6iNbkMVmptuT9c9mhsaua0":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":31,"type":32},[],"2026-06-02 06:39:32",178401556,[8,9,10,11],"突触","树突","细胞体","轴突",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},50,"53e1d2ef4961cca8eea3e23969ad2cb9","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F03a579384a6dc297c89809b582fcc767.png","默认课程","67a6e937ad614268a9cd3f37c9a4ce79","第二章作业","神经元之间的信息传递是通过什么结构实现的",[21,33,36,45,55,65,73,82,91,101],{"answer":22,"createTime":23,"id":24,"options":25,"question":30,"source":31,"type":32},[],"2026-06-02 13:46:02",178401555,[26,27,28,29],"相互矛盾","完全相同","毫无关系","相互促进","认知脑科学与人工神经网络的关系是","v1",0,{"answer":34,"createTime":5,"id":6,"options":35,"question":19,"source":31,"type":32},[],[8,9,10,11],{"answer":37,"createTime":23,"id":38,"options":39,"question":44,"source":31,"type":32},[],178401557,[40,41,42,43],"明斯基","麦卡洛克和皮茨","Hinton","罗森布拉特","单层感知机模型的提出者是",{"answer":46,"createTime":47,"id":48,"options":49,"question":54,"source":31,"type":32},[],"2026-06-02 14:34:48",178401558,[50,51,52,53],"异或问题","与问题","或问题","非问题","单层感知机模型无法解决的问题是",{"answer":56,"createTime":57,"id":58,"options":59,"question":64,"source":31,"type":32},[],"2026-06-01 07:37:02",178401559,[60,61,62,63],"参数更少","计算效率更高","非线性表达能力","线性表达能力","多层感知机模型的核心优势在于",{"answer":66,"createTime":67,"id":68,"options":69,"question":72,"source":31,"type":32},[],"2026-06-02 03:09:09",178401560,[70,42,43,71],"Yann LeCun","Yoshua Bengio","卷积神经网络(CNN)最早是由谁提出的",{"answer":74,"createTime":23,"id":75,"options":76,"question":81,"source":31,"type":32},[],178401561,[77,78,79,80],"提取特征","分类","防止过拟合","降维","卷积神经网络中,卷积层的主要作用是",{"answer":83,"createTime":5,"id":84,"options":85,"question":90,"source":31,"type":32},[],178401562,[86,87,88,89],"并行处理","序列处理","自适应学习","非线性映射","循环神经网络(RNN)的核心特性是",{"answer":92,"createTime":93,"id":94,"options":95,"question":100,"source":31,"type":32},[],"2026-06-02 12:18:35",178401563,[96,97,98,99],"输出门","更新门","遗忘门","输入门","LSTM模型中,用于决定哪些信息需要被保留的门是",{"answer":102,"createTime":103,"id":104,"options":105,"question":110,"source":31,"type":32},[],"2026-06-02 03:47:06",178401564,[106,107,108,109],"层归一化","多头注意力","残差连接","前馈网络","Transformer模型中,用于解决长距离依赖问题的关键技术是"]