Source data for Fig 2.

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few ‘strong’ synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including ‘background’ activity from the many ‘weak’ synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

神经元接收数千个突触的输入，这些突触的强度覆盖一个数量级的范围。值得注意的是，在小鼠新皮层中，少量“强”突触仅在调谐特性相似的细胞间形成，这表明此类突触决定了神经元的锋电位（spike）输出。这引出了如下科学问题：其他计算原语——包括源自大量“弱”突触的“背景”活动、短时程可塑性（short-term plasticity）以及时间因素——如何共同参与锋电位的产生过程。我们采用配对记录与细胞外刺激实验，测绘了桶皮层（barrel cortex）2/3层（layer 2/3, L2/3）锥体神经元（pyramidal neuron）之间形成的突触连接的兴奋性突触后电位（excitatory postsynaptic potential, EPSP）幅值与配对脉冲比（paired-pulse ratio）。尽管整体的短时程可塑性较弱，但强突触连接无一例外均呈现抑制性短时程可塑性。重要的是，我们未观测到单个神经元上存在突触特性聚类的证据。相反，汇聚至同一细胞的突触连接的EPSP幅值与配对脉冲比覆盖了L2/3中观测到的全部范围，这一发现对皮层滤波的理论模型构成了关键约束。为探究突触信息处理的不同计算原语如何协同作用以塑造锋电位输出，我们基于本实验数据与已发表的在体数据，构建了兴奋性L2/3环路中锥体神经元的计算模型。我们发现，强突触在持续激活过程中会发生显著抑制，其诱发关联锋电位的能力主要依赖于在体观测到的高时间同步性与高放电频率。然而，尽管存在此类抑制，其更大的EPSP幅值仍显著放大了信息传递效率与神经元响应性。综上，我们的研究成果为皮层神经元如何利用时间编码、突触特性与噪声之间的协同作用，将突触输入转化为锋电位，提供了一个精细化的理论框架。