How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.

中文翻译：

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从动态功能连接推断突触权重的时间演变

如何从同时记录的不同神经元活动之间的动态功能连接的测量中捕获突触权重的时间演变是系统神经科学中一个重要且开放的问题。在这里，我们报告了解决此问题的方法学进展。我们首先模拟了尖峰神经元的递归神经网络模型，该模型具有尖峰时间依赖性可塑性机制，可产生时变突触和功能耦合。然后，我们使用这些模拟来测试分析方法，这些分析方法从定向功能连接性测量（例如交叉协方差和传输熵）推断突触连接的固定和时变特性。我们发现，虽然交叉协方差和传输熵都提供了对网络中存在哪些突触及其通信延迟的可靠估计，但通过交叉协方差测量的动态功能连接性更好地捕捉了突触权重随时间的演变。我们还确定了在长时间记录期间（即几个小时）计算的静态功能连接的信息传输延迟测量如何改进动态功能连接的突触权重时间演化的较短时间尺度估计。这些结果提供了有关如何根据尖峰活动测量准确估计突触强度的时间变化的有用信息。我们还确定了在长时间记录期间（即几个小时）计算的静态功能连接的信息传输延迟测量如何改进动态功能连接的突触权重时间演化的较短时间尺度估计。这些结果提供了有关如何根据尖峰活动测量准确估计突触强度的时间变化的有用信息。我们还确定了在长时间记录期间（即几个小时）计算的静态功能连接的信息传输延迟测量如何改进动态功能连接的突触权重时间演化的较短时间尺度估计。这些结果提供了有关如何根据尖峰活动测量准确估计突触强度的时间变化的有用信息。