Embedded Ensemble Encoding

摘要 我们正在开发一种新的嵌入式集成编码(EEE)理论，用于哺乳动物的新皮质来统一来自 细胞和网络实验，并推断信息在大脑中如何处理的一般原理。我们的 调查人员组合包括一名理论家/建模师、一名实验师/建模师和一名建模师/ 神经信息学家。我们的理论是基于观察到的皮质锥体神经元产生 突触诱导的树突状平台电位，将单个神经元置于激活状态。这带来了 使神经元接近阈值，也降低了膜时间常数，使激活的细胞PNact很容易 并迅速跟随突触输入。我们假设这些被激活的细胞集合为被激活的 集合eACT，嵌入在柱的整体单元中。然后是乐团的第二次嵌入 基于eACT细胞间的同步刺激性。这个两次嵌入的合奏被表示为Esync，具有 Esync eACT。然后，区域内同步的尖峰编码为广泛分布的集合提供了基础 允许将多模式特征绑定到一致的对象感知的区域(基于 同步捆绑理论)。EEE理论对解释两种约束的同步性具有直接意义 理论，以及贝叶斯预测编码理论。开发的工具将用于通过以下方式促进其他项目 我们的最终用户：1.开发进一步简化的模型以进行更详细的分析(Mihalas)；2.开发模型 放置细胞理论(Kubie)；3.开发新的猕猴数据分析和刺激方案，用于脑机 界面开发(Francis)。 我们建议主要在多尺度模型中工作，以发展EEE理论的进一步细节，并使 SPECIfic预测。在神经科学中，与物理学不同，对大脑测量的详细预测必须是 通过在模拟中实例化理论而获得的，它允许实验者识别特定的尺度和 可以通过他们的实验手段获得的理论方面。我们的特殊fic目标是：1.开发一套 根据现有的实验数据和形态，建立第5层锥体细胞的单细胞模型，并进行测试 基底和根尖斜树突的输入/输出活动模式。生成模型预测，可以 在体外或体内实验中使用树突成像进行测试。2.搭建网络并进行fi环可变性测试。 编码密度、信息论信号fl透过率、图论度量。将执行Verifi阳离子 跨多个模型实例化。SPECIfic的实验预测将用于未来的模型验证。 3.通过模型共享、研讨会、教程和 课程。待开发和共享的工具包括用于模型参数fi设置的遗传算法、背景驱动 和激活-输入数据-套件，以及特定的fiC小区和网络模型。

Abstract We are developing a novel embedded-ensemble encoding (EEE) theory for mammalian neocortex to unify data from cell and network experiments, and to infer general principles of how information is processed in the brain. Our combination of investigators includes a theorist/modeler, an experimentalist/modeler and a modeler/ neuroinformatician. Our theory is based on the observation that cortical pyramidal neurons produce synaptically-induced dendritic plateau potentials that place an individual neuron into an activated state. This brings that neuron near to threshold, and also reduces membrane time constant, so that the activated cell PNact can readily and rapidly follow synaptic inputs. We hypothesize that ensembles of these activated cells provide the activated ensemble Eact, embedded in the overall cells of the column. There is then a second embedding of an ensemble based on synchronized spiking among the cells of Eact. This twice-embedded ensemble is denoted as Esync, with Esync Eact. Synchronized spike coding within area then provides the substrate for a broad distributed ensemble across areas that would allow the binding of multimodal features into coherent object perception (based on binding-by-synchrony theory). EEE theory has direct implications for interpretation of both binding-by-synchrony theory, and for theories of Bayesian predictive coding. Developed tools will be used to facilitate other projects through our end-users: 1. developing further reduced models for more detailed analysis (Mihalas); 2. develop models for place cell theory (Kubie); 3. develop new data analysis and stimulation protocols in macaque for use in brain-machine interface development (Francis). We propose to work primarily in a multiscale model both to develop further details of EEE theory, and to make specific predictions. In neuroscience, unlike in physics, detailed predictions for measures in the brain must be obtained by instantiating the theory in simulation, which allows the experimentalist to identify a particular scale and aspect of the theory that is accessible through their experimental measures. Our Specific Aims are: 1. Develop a set of single cell models of Layer 5 pyramidal cells based on available experimental data and morphologies, and test input/output activity patterns for inputs on basilar and apical oblique dendrites. Generate model predictions that can be tested in in vitro or in vivo experiments with dendritic imaging. 2. Build networks and test with firing variability, coding density, information-theoretic signal flow-through, graph-theoretic measures. Verification will be performed across multiple model instantiations. Specific experimental predictions will be made for future model validation. 3. Disseminate theory, models and experimental predictions through model sharing, workshops, tutorials, and courses. Tools to be developed and shared include genetic algorithms for model parameter fitting, background-driving and activation-input data-suites, and specific cell and network models.