Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.
Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.
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DOI：
10.1371/journal.pcbi.1003138
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发表时间：
2013
影响因子：
4.3
通讯作者：
Eden U
中科院分区：
文献类型：
--
作者：
Gerhard F;Kispersky T;Gutierrez GJ;Marder E;Kramer M;Eden U
识别神经元之间突触相互作用的结构和动力学是理解神经网络动力学的第一步。传统上通过使用靶向刺激和配对记录或通过事后组织学来推断突触连接的存在。最近，已经提出了因果网络推理算法来直接从电生理信号（例如细胞外记录的尖峰活动）推断连接性。通常，这些算法尚未在已知实际电路的神经生理数据集上进行验证。最近的工作表明，传统的网络推理算法的基础上的线性模型通常无法识别正确的耦合的一个小的中央模式产生电路的口胃神经节的螃蟹北。在这项工作中，我们发现，点过程模型观察到的尖峰列车可以指导推理的相对连接估计，匹配已知的生理连接的中央模式发生器的阈值的选择。我们阐明了必要的步骤，以获得忠实的连接估计模型，结合尖峰列车性质的数据。然后，我们应用该模型来测量响应于两种药物干预的有效连接模式的变化，这两种药物干预会影响内在神经动力学和突触传递。我们的研究结果提供了第一个成功的应用网络推理算法的电路，神经元之间的实际生理突触是已知的。这里提出的点过程方法可以很好地推广到更大的网络，并且可以描述神经种群的统计数据。总的来说，我们表明先进的统计模型可以表征有效的网络结构、破译底层网络动态并估计信息处理能力。为了理解神经回路如何控制行为，我们必须了解两件事。第一，组成电路的神经元是如何连接的，第二，神经元及其连接在学习后或响应神经调质时如何变化。神经元的连通性很难通过实验来确定，而神经元的活动通常可以很容易地测量。我们描述了一个统计模型来估计电路连接直接从测量的活动模式。我们使用所观察到的尖峰之间的时序关系来预测同时观察到的神经元之间的突触相互作用。模型估计为每个预测的连接提供曲线，该曲线表示一个电路元件有效地影响另一个电路元件的强度以及时间延迟。这些曲线类似于生物神经元的膜电位水平的突触相互作用，并且共享它们的一些特征，例如抑制性或兴奋性。我们测试我们的方法从幽门电路的记录在螃蟹口胃神经节，一个小电路，其连接是完全已知的事先，并发现预测的电路匹配的生物-其他技术未能实现的结果。此外，我们表明，影响电路的药物操作揭示了这种技术。这些结果说明了我们的分析方法推断连接神经尖峰活动的效用。
Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities. To appreciate how neural circuits control behaviors, we must understand two things. First, how the neurons comprising the circuit are connected, and second, how neurons and their connections change after learning or in response to neuromodulators. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured. We describe a statistical model to estimate circuit connectivity directly from measured activity patterns. We use the timing relationships between observed spikes to predict synaptic interactions between simultaneously observed neurons. The model estimate provides each predicted connection with a curve that represents how strongly, and at which temporal delays, one circuit element effectively influences another. These curves are analogous to synaptic interactions of the level of the membrane potential of biological neurons and share some of their features such as being inhibitory or excitatory. We test our method on recordings from the pyloric circuit in the crab stomatogastric ganglion, a small circuit whose connectivity is completely known beforehand, and find that the predicted circuit matches the biological one — a result other techniques failed to achieve. In addition, we show that drug manipulations impacting the circuit are revealed by this technique. These results illustrate the utility of our analysis approach for inferring connections from neural spiking activity.
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DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
DOI：
10.1126/science.1171402
发表时间：
2009-05-29
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gregoriou GG;Gotts SJ;Zhou H;Desimone R
通讯作者：
Desimone R
影响因子：
--
作者：
Grillner, S;Ekeberg, Ö;Wallén, P
通讯作者：
Wallén, P
影响因子：
2.5
作者：
AERTSEN, AMHJ;GERSTEIN, GL;PALM, G
通讯作者：
PALM, G
DOI：
10.1523/jneurosci.2998-09.2010
发表时间：
2010-03-31
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Goaillard JM;Taylor AL;Pulver SR;Marder E
通讯作者：
Marder E