Mikula Shawn, Niebur Ernst
Krieger Mind/Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.
Neural Comput. 2003 Oct;15(10):2339-58. doi: 10.1162/089976603322362383.
In this letter, we extend our previous analytical results (Mikula & Niebur, 2003) for the coincidence detector by taking into account probabilistic frequency-dependent synaptic depression. We present a solution for the steady-state output rate of an ideal coincidence detector receiving an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero correlation (independent processes) to perfect correlation (identical processes). Synapses vary their efficacy probabilistically according to the observed depression mechanisms. Our results show that synaptic depression, if made sufficiently strong, will result in an inverted U-shaped curve for the output rate of a coincidence detector as a function of input rate. This leads to the counterintuitive prediction that higher presynaptic (input) rates may lead to lower postsynaptic (output) rates where the output rate may fall faster than the inverse of the input rate.
在这封信中，我们通过考虑概率性频率依赖的突触抑制，扩展了我们之前关于重合检测器的分析结果（米库拉和尼布尔，2003年）。我们给出了一个理想重合检测器稳态输出率的解决方案，该检测器接收任意数量具有相同二项分布计数分布（其中包括泊松统计作为特殊情况）和相同任意成对互相关的输入尖峰序列，互相关从零相关（独立过程）到完全相关（相同过程）。突触根据观察到的抑制机制概率性地改变其效能。我们的结果表明，如果突触抑制足够强，重合检测器的输出率将作为输入率的函数呈现倒U形曲线。这导致了一个违反直觉的预测，即较高的突触前（输入）率可能导致较低的突触后（输出）率，其中输出率下降的速度可能比输入率的倒数更快。
