Embedded Ensemble Encoding

嵌入式集成编码

• 批准号: 9170558
• 负责人: SRDJAN D ANTIC
• 金额: $ 49.1万
• 依托单位: SUNY DOWNSTATE MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-27 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170558
• 关键词: Alzheimer' s Disease; Apical; Area; Autistic Disorder; Automobile Driving; Back; Binding; Brain; Brain Diseases; Cell model; Cell physiology; Cells; Code; Communities; Computer Simulation; Cortical Column; Data; Data Analyses; Dendrites; Development; Disease; Educational workshop; Entropy; Environment; Equation; Experimental Models; Failure; Future; Genetic Programming; Grant; Graph; High Performance Computing; Hybrids; Image; In Vitro; Individual; Information Theory; Macaca; Measures; Membrane; Modeling; Morphology; Neocortex; Neurons; Neurosciences; Organ; Output; Pattern; Perception; Physics; Play; Process; Protocols documentation; Published Comment; Pyramidal Cells; Pythons; Research Personnel; Scheme; Schizophrenia; Signal Transduction; Software Tools; Synapses; Techniques; Testing; Thinking; Time; Validation; Work; abstracting; arm; base; brain machine interface; density; design; graph theory; hippocampal pyramidal neuron; in vivo; interest; model development; multi-scale modeling; network models; novel; object perception; relating to nervous system; research study; simulation; theories; tool

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.

抽象的 我们正在为哺乳动物新皮层开发一种新颖的嵌入式编码（EEE）理论，以统一数据 细胞和网络实验，并推断大脑中信息处理方式的一般原则。我们的 研究人员的组合包括理论家/建模者，实验家/建模者和建模者/ 神经信息学家。我们的理论是基于皮质锥体神经元产生的观察 将单个神经元置于活化状态的突触引起的树突状平原电位。这带来了 该神经元接近阈值，还减少了膜时间常数，因此活化的细胞PNACT可以轻松 并迅速遵循合成输入。我们假设这些激活的细胞的集合提供了激活 集合EACT，嵌入了色谱柱的整体细胞中。然后有第二个集合的嵌入 基于EACT细胞之间的同步峰值。这个两次装饰的合奏被称为esync， esync eact。然后在区域内同步尖峰编码，然后为宽分布式合奏提供基板 在允许多模式特征结合到相干对象感知中的区域（基于 通过同步理论结合）。 EEE理论对解释两种绑定的直接含义 理论，以及贝叶斯预测性编码的理论。开发的工具将用于通过 我们的最终用户：1。开发进一步的减少模型以进行更详细的分析（Mihalas）； 2。开发模型 位置细胞理论（Kubie）； 3。在猕猴中开发新的数据分析和刺激方案，以用于脑机 界面开发（弗朗西斯）。 我们建议主要在多尺度模型中工作，以开发EEE理论的进一步细节，并使 特定预测。在神经科学中，与物理学不同，大脑中的措施的详细预测必须是 通过实例化模拟理论获得的，该理论允许实验者识别特定的量表和 通过实验措施可以访问的理论方面。我们的特定目的是：1。开发一套 基于可用的实验数据和形态的5层锥体细胞的单细胞模型，并测试 基底和顶端斜树突上输入的输入/输出活动模式。生成模型预测 在体外或体内进行树突成像实验。 2。建立网络并使用filter变异性测试， 编码密度，信息理论信号流，图理论测量。将进行验证 跨多个模型实例。将对未来的模型验证进行特定的实验预测。 3。通过模型共享，研讨会，教程和 课程。要开发和共享的工具包括用于模型参数拟合，背景驱动的遗传算法 和激活输入数据媒体，以及特定的单元格和网络模型。