标题： 使用伯努利自回归部分线性可加模型识别神经连接性 显示英文标题

标题： Identifying Neural Connectivity using Bernoulli Autoregressive Partially Linear Additive Models

摘要： 表征脉冲神经元之间的相互作用是理解记忆、感知和决策等认知过程的核心。 在本工作中，我们考虑从非平稳的高维脉冲序列数据中推断脑网络中的连接性问题。 在这些数据的分箱脉冲计数表示下，我们提出了一种伯努利自回归部分线性可加（BAPLA）模型，以识别神经元群体的有效连接性。 使用正则化最大似然估计器获得模型参数的估计，其中使用$\ell_1$惩罚来找到稀疏且可解释的神经元相互作用估计。 我们还通过向模型中添加一个非参数趋势来考虑非平稳的发放率，并提供一种推断程序来量化与我们估计的神经元相互作用网络相关的不确定性。 我们使用合成数据来评估BAPLA方法的性能，突出其在各种情况下检测兴奋性和抑制性相互作用的能力。 最后，我们将我们的方法应用于来自DANDI存档的神经脉冲数据集，研究神经过程在各种刺激-反应类型的神经科学实验中的相互作用。 显示英文摘要

摘要： Characterising the interactions between spiking neurons is central to our understanding of cognitive processes such as memory, perception and decision making. In this work, we consider the problem of inferring connectivity in the brain network from non-stationary high-dimensional spike train data. Under a binned spike count representation of these data, we propose a Bernoulli autoregressive partially linear additive (BAPLA) model to identify the effective connectivity of a population of neurons. Estimates of the model parameters are obtained using a regularised maximum likelihood estimator, where an $\ell_1$ penalty is used to find sparse and interpretable estimates of neuronal interactions. We also account for non-stationary firing rates by adding a non-parametric trend to the model and provide an inference procedure to quantify the uncertainty associated with our estimated networks of neuronal interactions. We use synthetic data to assess the performance of the BAPLA method, highlighting its ability to detect both excitatory and inhibitory interactions in various settings. Finally, we apply our method to a neural spiking dataset from the DANDI archive, where we study the interactions of neural processes in reaction to various stimulus-response type neuroscience experiments.