It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

表征细胞集合中单个神经元的尖峰活动非常有趣。许多不同的机制，例如突触耦合和自身及其邻居的尖峰活动，驱动细胞的放电特性。尽管这是一个广泛研究的建模问题，但仍有空间通过嵌入先前模型的简化来开发建模解决方案。第一个捷径是先前模型中的突触耦合机制不会复制突触响应的复杂动态。第二个是这些模型中突触连接的数量比实际神经元小一个数量级。在这项研究中，我们通过整合更准确的突触模型来推动这一障碍，并提出了一种系统识别解决方案，该解决方案可以扩展到包含数百个突触连接的网络。尽管一个神经元有数百个突触连接，但只有这些连接的一个子集对其尖峰活动有显着贡献。因此，我们假设突触连接是稀疏的，为了表征这些动态，我们提出了一个贝叶斯点过程状态空间模型，它让我们将正则化技术中突触连接的稀疏性合并到我们的框架中。我们开发了一个扩展的期望最大化。算法来估计所提出模型的自由参数，并演示了该方法在估计许多动态突触连接参数的问题中的应用。然后，我们通过一个模拟示例，该示例由一系列参数值的动态突触组成，并表明可以使用我们的方法估计模型参数。我们还展示了所提出的算法在包含 96 个突触前连接的细胞内数据中的应用，并使用拟合优度的组合评估了我们方法的估计准确性。