Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common generalized linear model to infer both short- and long-term changes in the coupling between a pre- and postsynaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights (1) for both depressing and facilitating synapses, (2) with a variety of long-term changes (including realistic changes, such as due to STDP), (3) with a range of pre and postsynaptic firing rates, and (4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission.

由于短期和长期可塑性的结合，突触在多个时间尺度上发生变化，从几毫秒到几分钟不等。在这里，我们开发了通用广义线性模型的扩展，以根据观察到的尖峰活动推断突触前和突触后神经元之间耦合的短期和长期变化。我们使用依赖于突触前尖峰时间的加性效应对短期突触可塑性进行建模，并使用点过程自适应平滑对突触权重和基线放电率的长期变化进行建模。使用模拟，我们首先表明该模型可以准确地恢复时变突触权重 (1) 用于抑制和促进突触，(2) 具有各种长期变化（包括现实变化，例如由于 STDP），(3) 具有一系列突触前和突触后放电率，以及 (4) 兴奋性和抑制性突触。然后我们将我们的模型应用于两个实验记录的假定突触连接。我们发现，同时跟踪突触权重的快速变化、突触权重的缓慢变化以及无法解释的基线放电变化是必不可少的。忽略这些因素中的任何一个都可能导致对其他因素的虚假推断。总之，该模型提供了一个灵活的框架来跟踪尖峰传输的短期和长期变化。基线射击中无法解释的变化是必不可少的。忽略这些因素中的任何一个都可能导致对其他因素的虚假推断。总之，该模型提供了一个灵活的框架来跟踪尖峰传输的短期和长期变化。基线射击中无法解释的变化是必不可少的。忽略这些因素中的任何一个都可能导致对其他因素的虚假推断。总之，该模型提供了一个灵活的框架来跟踪尖峰传输的短期和长期变化。