Laboratory of Applied Mathematics, Mount Sinai School of Medicine, New York, NY 10029, USA.
Neural Comput. 2011 Jul;23(7):1675-703. doi: 10.1162/NECO_a_00139. Epub 2011 Apr 14.
We derive a model of a neuron's interspike interval probability density through analysis of the first passage problem. The fit of our expression to retinal ganglion cell laboratory data extracts three physiologically relevant parameters, with which our model yields input-output features that conform to laboratory results. Preliminary analysis suggests that under common circumstances, local circuitry readjusts these parameters with changes in firing rate and so endeavors to faithfully replicate an input signal. Further results suggest that the so-called principle of sloppy workmanship also plays a role in evolution's choice of these parameters.
我们通过分析首次通过问题推导出神经元的脉冲间隔概率密度模型。我们的表达式对视网膜神经节细胞实验室数据的拟合提取了三个生理相关参数，利用这些参数，我们的模型产生了符合实验室结果的输入-输出特征。初步分析表明，在常见情况下，局部回路会根据放电率的变化调整这些参数，从而努力真实地复制输入信号。进一步的结果表明，所谓的马虎工作原理在进化对这些参数的选择中也起到了一定的作用。
