Departments of Psychological Sciences
Statistics, University of Connecticut, Storrs, Connecticut 06269.
J Neurosci. 2022 Nov 16;42(46):8608-8620. doi: 10.1523/JNEUROSCI.0808-22.2022. Epub 2022 Sep 28.
Many controlled studies have demonstrated how postsynaptic responses to presynaptic spikes are not constant but depend on short-term synaptic plasticity (STP) and the detailed timing of presynaptic spikes. However, the effects of short-term plasticity (depression and facilitation) are not limited to short, subsecond timescales. The effects of STP appear on long timescales as changes in presynaptic firing rates lead to changes in steady-state synaptic transmission. Here, we examine the relationship between natural variations in the presynaptic firing rates and spike transmission Using large-scale spike recordings in awake male and female mice from the Allen Institute Neuropixels dataset, we first detect putative excitatory synaptic connections based on cross-correlations between the spike trains of millions of pairs of neurons. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy and find that efficacy varies substantially on slow (∼1 min) timescales over the course of these recordings. For many connections, the efficacy fluctuations are correlated with fluctuations in the presynaptic firing rate. To understand the potential mechanisms underlying this relationship, we then model the detailed probability of postsynaptic spiking on a millisecond timescale, including both slow changes in postsynaptic excitability and monosynaptic inputs with short-term plasticity. The detailed model reproduces the slow efficacy fluctuations observed with many putative excitatory connections, suggesting that these fluctuations can be both directly predicted based on the time-varying presynaptic firing rate and, at least partly, explained by the cumulative effects of STP. The firing rates of individual neurons naturally vary because of stimuli, movement, and brain state. Models of synaptic transmission predict that these variations in firing rates should be accompanied by slow fluctuations in synaptic strength because of short-term depression and facilitation. Here, we characterize the magnitude and predictability of fluctuations in synaptic strength using large-scale spike recordings. For putative excitatory connections from a wide range of brain areas, we find that typical synaptic efficacy varies as much as ∼70%, and in many cases the fluctuations are well described by models of short-term synaptic plasticity. These results highlight the dynamic nature of synaptic transmission and the interplay between synaptic strength and firing rates in awake animals.
许多对照研究已经证明,突触后对突触前尖峰的反应并非恒定不变,而是取决于短期突触可塑性(STP)和突触前尖峰的详细时间。然而,短期可塑性(压抑和易化)的影响不仅限于短暂的亚秒时间尺度。STP 的影响出现在长时间尺度上,因为突触前放电率的变化导致稳态突触传递的变化。在这里,我们研究了突触前放电率的自然变化与尖峰传递之间的关系。
使用艾伦研究所神经像素数据集在清醒雄性和雌性小鼠中进行的大规模尖峰记录,我们首先基于数百万对神经元的尖峰列车之间的互相关检测潜在的兴奋性突触连接。对于检测到短暂兴奋性效应的子集对,我们使用基于模型的方法来跟踪突触效能的波动,发现效能在这些记录过程中以缓慢(约 1 分钟)的时间尺度上发生显著变化。对于许多连接,效能波动与突触前放电率的波动相关。
为了理解这种关系的潜在机制,我们然后在毫秒时间尺度上对突触后放电的详细概率进行建模,包括突触后兴奋性的缓慢变化和具有短期可塑性的单突触输入。详细模型再现了许多潜在兴奋性连接的缓慢效能波动,表明这些波动不仅可以基于时变的突触前放电率直接预测,而且至少部分可以通过 STP 的累积效应来解释。
单个神经元的放电率由于刺激、运动和大脑状态而自然变化。突触传递的模型预测,由于短期压抑和易化,这些放电率的变化应该伴随着突触强度的缓慢波动。在这里,我们使用大规模尖峰记录来描述突触强度波动的幅度和可预测性。对于来自广泛脑区的潜在兴奋性连接,我们发现典型的突触效能变化高达约 70%,并且在许多情况下波动可以很好地用短期突触可塑性模型来描述。
这些结果强调了突触传递的动态性质以及在清醒动物中突触强度和放电率之间的相互作用。