首页> 外文期刊>Philosophical Transactions of the Royal Society of London, Series B. Biological Sciences >Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans
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Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

机译:神经动力学的功能互联器:Caenorhabdisigans神经元网络的概率图形模型

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摘要

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios.
机译:我们提出一种方法来代表神经元网络动态作为概率图形模型(PGM)。为了构建PGM,我们收集由神经网络产生的时间序列的神经元响应,并使用奇异值分解来获得时间序列数据的低维投影。然后,我们从投影中提取主导模式以获得成对依赖信息并为完整网络创建图形模型。结果模型是一种功能性连接,捕获刺激如何通过网络传播,因此代表神经元和刺激之间的因果依赖性。我们将我们的方法应用于CaenorhabditiseDegrans躯体神经系统的模型,以验证和展示我们的方法。研究了C.杆状内裤神经系统的结构和动态,并获得了一种产生神经元反应的模型。得到的PGM使我们能够获得并验证潜在的神经元途径,以了解已知的行为场景,并检测新颖场景的可能途径。

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