Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.
Cereb Cortex. 2013 Feb;23(2):293-304. doi: 10.1093/cercor/bhs006. Epub 2012 Feb 7.
Cortical synaptic strengths vary substantially from synapse to synapse and exhibit a skewed distribution with a small fraction of synapses generating extremely large depolarizations. Using multiple whole-cell recordings from rat hippocampal CA3 pyramidal cells, we found that the amplitude of unitary excitatory postsynaptic conductances approximates a lognormal distribution and that in the presence of synaptic background noise, the strongest fraction of synapses could trigger action potentials in postsynaptic neurons even with single presynaptic action potentials, a phenomenon termed interpyramid spike transmission (IpST). The IpST probability reached 80%, depending on the network state. To examine how IpST impacts network dynamics, we simulated a recurrent neural network embedded with a few potent synapses. This network, unlike many classical neural networks, exhibited distinctive behaviors resembling cortical network activity in vivo. These behaviors included the following: 1) infrequent ongoing activity, 2) firing rates of individual neurons approximating a lognormal distribution, 3) asynchronous spikes among neurons, 4) net balance between excitation and inhibition, 5) network activity patterns that was robust against external perturbation, 6) responsiveness even to a single spike of a single excitatory neuron, and 7) precise firing sequences. Thus, IpST captures a surprising number of recent experimental findings in vivo. We propose that an unequally biased distribution with a few select strong synapses helps stabilize sparse neuronal activity, thereby reducing the total spiking cost, enhancing the circuit responsiveness, and ensuring reliable information transfer.
皮质突触强度在突触间有很大差异，呈偏态分布，一小部分突触产生极大的去极化。我们使用来自大鼠海马 CA3 锥体神经元的多个全细胞记录，发现单位兴奋性突触后电流的幅度近似于对数正态分布，并且在存在突触背景噪声的情况下，最强的突触分数可以在单个突触前动作电位的情况下引发突触后神经元的动作电位，这种现象称为锥体间尖峰传递（IpST）。IpST 概率达到 80%，取决于网络状态。为了研究 IpST 如何影响网络动力学，我们模拟了一个嵌入少数有效突触的递归神经网络。与许多经典神经网络不同，这个网络表现出与体内皮质网络活动相似的独特行为。这些行为包括以下几点：1） 偶尔的持续活动，2） 单个神经元的发放率近似于对数正态分布，3） 神经元之间的异步尖峰，4） 兴奋和抑制之间的净平衡，5） 对外部干扰具有鲁棒性的网络活动模式，6） 即使单个兴奋性神经元的单个尖峰也能产生反应，以及 7） 精确的发放序列。因此，IpST 捕获了体内许多令人惊讶的最新实验发现。我们提出，少数选择的强突触的不均衡偏置分布有助于稳定稀疏的神经元活动，从而降低总发放成本，增强电路响应，并确保可靠的信息传递。
