Ghanbari Abed, Malyshev Aleksey, Volgushev Maxim, Stevenson Ian H
Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia.
PLoS Comput Biol. 2017 Sep 5;13(9):e1005738. doi: 10.1371/journal.pcbi.1005738. eCollection 2017 Sep.
Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful tools for characterizing short-term plasticity from multi-electrode spike recordings in vivo.
短期突触可塑性（STP）通过在从毫秒到几秒的时间尺度上可逆地改变神经元之间连接的有效强度，对神经回路中的信息处理产生关键影响。传统上，STP是通过对突触后电位或突触前尖峰诱发的电流进行细胞内记录来研究的。然而，STP也会影响突触后尖峰的统计特性。在此，我们提出了两种基于模型的方法，仅从突触前和突触后尖峰观测数据来估计突触权重和短期可塑性。我们扩展了一个广义线性模型（GLM），该模型将突触后尖峰预测为观测到的突触前和突触后尖峰的函数，并允许连接强度（GLM中的耦合项）根据突触前尖峰的历史随时间变化。我们的第一个模型假设STP遵循Tsodyks - Markram对囊泡耗竭和恢复的描述。在第二个模型中，我们引入了STP的函数描述，其中我们将耦合项估计为突触前峰间间隔的生物物理无约束函数。为了验证模型，我们使用具有已知突触动力学的突触前和突触后神经元的尖峰来测试STP估计的准确性。我们首先使用2/3层锥体神经元对具有不同类型STP的模拟突触前输入的反应来测试我们的模型，然后使用模拟尖峰序列来检查尖峰频率适应、随机囊泡释放、尖峰分类错误和共同输入的影响。我们发现，仅使用尖峰观测数据，两种基于模型的方法都可以准确地重建不同类型STP的突触前输入的时变突触权重。我们的模型还捕捉到了突触后尖峰对突触前尖峰在短峰间间隔与长峰间间隔后的反应差异，这与丘脑皮质连接的报道结果相似。因此，这些模型可能是用于从体内多电极尖峰记录中表征短期可塑性的有用工具。
