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Cryogen 是一个用来生成静态网站的工具，简单而且自动化。Cryogen 使用用 Clojure 语言开发。 使用非常简单： 安装 Leiningen 创建新项目 启动服务器并在浏览器打开localhost:3000 特性： 带有Markdown（默认）或AsciiDoc的博客文章和页面 标签 目录生成 纯HTML页面模板 代码语法高亮 Disqus支持 网站地图生成 RSS提要生成 Sass / SCSS编译 Klipse整合 命令： ~ $ lein new cryogen my-blog ~ $ cd my-blog ~/my-blog $ lein r.
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2. 伤左半球不太影响speech recognition(由声音查lexicon)，因为右侧有备胎。伤左半球一般不能识别音节(元音加辅音，一般为声音波形的packet)，因为背流处理识别音节只单左侧。伤左半球一般造成conduction失语症(如何使用随机振荡器确定Pocket Option上的趋势反转 如何使用随机振荡器确定Pocket Option上的趋势反转 数数时1234#@#678，想说5但是说不出来)。有病人能识别音节(背流正常腹流伤)但是不理解意义(比如外国人说话每个单词都清楚放一起不知道讲什么)，有病人能识语义(腹流正常背流受伤)但是不识别音节(如妈妈说吃苹果，你就按命令去吃，但是问你妈妈说苹果了吗，你不知道)。
4. 视觉背流(where流)如何使用随机振荡器确定Pocket Option上的趋势反转 在parietal与躯体感觉区接触，该处神经元不仅对眼前的苹果放电，闭上眼睛去reach 苹果也放电（即神经元同时受到视觉motor effecter影响）。optic ataxia共济失调，能知道眼前物体是方的，但是够不到。visual agosia，能够到物体但是不知道物体张什么样。
6. Fig4：reproduce speech。前向通路：双侧STS处理声音，spt将声音表达map到motor articulatory区。feedback monitoring还可以纠正发音错误（conduction失语症就是Sensory不能提供guidance给speech sound sequencimg）。应该有working memory功能保持默读声音或音素时序表达。
7 Spt为声道相关的sensomotor integration。音乐家听完曲子后，用mental哼哼方式重复spt放电明显，用mental play keyboard方式重复spt放电明显弱。Spt为PlanumTemporal的一部分，接受多模态输入(如speech、tone、music、spatial sound、visual speech、sign image等)，神经元根据输出端的motor effecter组织在一起。
- Moser夫妇因grid cell的发现而获得2014年诺贝尔医学奖。本文作者为妻子May-Britt Moser。
- 本文研究海马CA3(330个)CA1(487个)神经元同时放电于两种情况：1 cue变化（盒子黑白色、方圆型）地点不变（如在厕所做实验）。2地点变化（厕所或厨房）但cue不变（黑色方盒子）
- Fig1. （地点不变如在厕所）盒子变换形状或者颜色，每个CA3神经元的放电rate map的pattern不变，但相同rate map pattern下整体强度受控或者编码盒子颜色和形状
- Fig2. （cue不变，如老鼠在黑色透明方盒里面跑）实验地点分别在A厕所B厨房，发现CA3或者CA1的具体神经元rate map pattern完全不同（有报道说有正交关系）
- Fig3. 分四种情况：a（control组）同样是白盒子同样在厕所，分别早晚各做一次实验，发现各神经元的两次实验的rate map pattern相关度都很高，pattern质心差别甚微，放电率差别甚微，方盒各点Population Vector相关度（所有神经元rate map摞层3D，看两次实验的每个位点的矢量相关系数）巨高。b（盒子颜色为黑白的两次实验），与A相似但是盒子颜色调节map整体强度，使得相关度下降。c（盒子形状为方圆两种），与B类似但是形状调节map整体强度能力更强，相关度下降更猛烈。d（实验场所为厕所和厨房），map pattern完全不同甚至正交，所有相关接近于零。顶图为CA3，底图为CA1。CA3放电rate受到颜色形状调节更为明显。
- cue就是episodic event、实验地点就是spatial调节。海马神经元群能同时encode空间位置（如Fig1C，众神经元Cell1-9放电能tile老鼠盒子中的位置）和事件特征（盒子颜色形状，prospective目的以及retrospective经历，由多neuron各自map整体放电强度来encode）
- Tolman认知地图：学习stimulus-response关系来构建关于’世界’认知的地图（地图样的因果associations）来做plan和推理等实现goal。该认知地图不限于spatial map，亦可是因果关系如(stimulus开水，action喝，outcome痛)喝开水会痛。
2. 认知地图得a识别分解世界为离散状态，b学并建立各状态间联系，cEncode状态到价值的好坏，d基于此做plan与结果预测。整体用多结构分布式model-based goaldirected完成于海马和OFC
3. 海马：为mapping（spatial或抽象因果关系），bind对外部的sensory features与内部情绪motivation价值形成关系database。encode外部世界做认知推理
4. OFC：基于奖励/价值的行为选择。形成并维持task state（尤其非直接observable的task，如经过trials后意识到每次左转有奖励）。意识到task变化。task state 的认知地图
5. Fig1 a-c为海马placecell（PC）对位置的coding，多PC顺次放电代表所经历的trajectory。公共段的world 如何使用随机振荡器确定Pocket Option上的趋势反转 如何使用随机振荡器确定Pocket Option上的趋势反转 state相同，左、右转有各自海马n放电表达不同状态。有non-spatial factor可以影响PC放电。 Fig1d-f OFC在decision making task中的放电表达。OFC.n放电于（cue，action，outcome）来encode当前task state。对特定味道某OFC.n放电与否依赖于其所存在的整个sequence。
6. 海马PC反应于环境整体拓扑（无奖励相关Reinforcement），也受到non-spatial factor调节（如从左边经过A地某cell放电，从右边经过某地则不放电）。能prospectively explore world model。能直接experience能inference。
7。 OFC能学response-outcome关系，识别当前outcome-value变化。能parse world为离散state，并assign value给每个state。能track task的variables（如买房子时有人要考虑孩子上学的因素），能prospective不全task info来前瞻结果before experiencing。伤mOFC不影响explicit task行为（左箭头就左拐），影响unobservable task行为（多次尝试后知道左转有奖励）。OFC能将大量variables（如买房子考虑地段价位朝向楼层学区等）融为一个worth value。该价值源于state表达用于指导行为。
8. BOX: 海马OFC有直接相互作用，海马经由subiculum传CA1信息给OFC，OFC需要经由旁海马（perihinal， posterihinal，entorhinal）传信息。OFC与海马还经由thalamus reunion核来协调信息流方向。海马OFC信号支配VTA，VTA为多巴元key于associative学习，用奖励期望误差来调整突触以期做正确行为选择来获得更多未来奖励。海马OFC信号支配vStriatum，其ramp cell encode reinforcement的时间。（伤海马影响奖励时间预测，伤OFC影响奖励幅度预测）
9. Fig2a-b鼠用’试错’的方式学策略（如左转有奖励）。会在choicepoint处做VTE(vicarious trial and error),海马PC做look ahead模拟不同path，OFC对相应模拟path做评估’有无奖励’。海马PC放电做prospective和retrospective模拟，PC放电受到context调节（从何方向来，到何方向去）。OFC全程放电敏于path是否有奖励，task switch中OFC能integrate奖励与response信息来更新rule task表达。
10. 稳定时海马OFC coherent oscillation于theta波段（5-12Hz）。rule switch时coherence破坏，随着试错而慢慢提升coherence。
12. Fig2c precondition期中性stimulus A铃声出现后B铃声再出现，海马建立两者association。condition期B出现后面包出现，海马建立两者之间联系，OFC建立对面包和奖励expectancy以及B铃声与奖励expectancy联系。test期A铃声同过推理也能产生奖励expectancy。
13. 海马key于associative scaffolding （各种联系）建立，OFC 基于scaffolding于来做奖励预测来drive response。
14. 海马善于link 如何使用随机振荡器确定Pocket Option上的趋势反转 info于时序sequence，反应所学的connection，使得海马能encode retrieve并且explore mental model of state space. 海马做抽象associative learning。OFC更根于生物重要性，自身需求，如学捕食，devaluation（发霉吃坏过肚子）的食物。
15. OFC发信息给海马，使得海马敏于奖励、movtivation、goal，使海马做goal-dependent navigation。海马发space和关系信息给OFC，使得OFC能bind outcome期望与相应的path/position/味道序列等，这样就能找到合适path等behave来得到最大value outcome。