Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
PLoS Comput Biol. 2013;9(7):e1003138. doi: 10.1371/journal.pcbi.1003138. Epub 2013 Jul 11.
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.
确定神经元之间的突触相互作用的结构和动态是理解神经网络动态的第一步。突触连接的存在传统上是通过靶向刺激和配对记录或事后组织学来推断的。最近，因果网络推断算法已经被提出，以便直接从电生理信号（如细胞外记录的尖峰活动）推断连接。通常，这些算法尚未在神经生理学数据集上进行验证，而这些数据集的实际电路是已知的。最近的工作表明，基于线性模型的传统网络推断算法通常无法识别螃蟹 Cancer borealis 的 stomatogastric ganglion 中一个小中央模式生成电路的正确耦合。在这项工作中，我们表明，观察到的尖峰列车的点过程模型可以指导相对连接估计的推断，这些估计与中央模式生成器的已知生理连接相匹配，直到选择阈值。我们阐明了从模型中得出忠实连接估计的必要步骤，该模型包含数据的尖峰列车性质。然后，我们将模型应用于测量对两种药理学干预的有效连接模式的变化，这些干预会影响内在神经动力学和突触传递。我们的结果提供了第一个成功应用网络推断算法的例子，该算法适用于已知神经元之间实际生理突触的电路。这里提出的点过程方法很好地推广到更大的网络，可以描述神经群体的统计数据。总的来说，我们表明，先进的统计模型允许对有效网络结构进行特征化，解析潜在的网络动态并估计信息处理能力。
