Neural Information Processing Group, Technische Universität Berlin, Berlin, Germany; and.
J Neurophysiol. 2014 Mar;111(5):939-53. doi: 10.1152/jn.00586.2013. Epub 2013 Oct 30.
Many types of neurons exhibit spike rate adaptation, mediated by intrinsic slow K(+) currents, which effectively inhibit neuronal responses. How these adaptation currents change the relationship between in vivo like fluctuating synaptic input, spike rate output, and the spike train statistics, however, is not well understood. In this computational study we show that an adaptation current that primarily depends on the subthreshold membrane voltage changes the neuronal input-output relationship (I-O curve) subtractively, thereby increasing the response threshold, and decreases its slope (response gain) for low spike rates. A spike-dependent adaptation current alters the I-O curve divisively, thus reducing the response gain. Both types of an adaptation current naturally increase the mean interspike interval (ISI), but they can affect ISI variability in opposite ways. A subthreshold current always causes an increase of variability while a spike-triggered current decreases high variability caused by fluctuation-dominated inputs and increases low variability when the average input is large. The effects on I-O curves match those caused by synaptic inhibition in networks with asynchronous irregular activity, for which we find subtractive and divisive changes caused by external and recurrent inhibition, respectively. Synaptic inhibition, however, always increases the ISI variability. We analytically derive expressions for the I-O curve and ISI variability, which demonstrate the robustness of our results. Furthermore, we show how the biophysical parameters of slow K(+) conductances contribute to the two different types of an adaptation current and find that Ca(2+)-activated K(+) currents are effectively captured by a simple spike-dependent description, while muscarine-sensitive or Na(+)-activated K(+) currents show a dominant subthreshold component.
许多类型的神经元表现出尖峰率适应现象，这是由内在的慢 K(+)电流介导的，这种电流有效地抑制了神经元的反应。然而，这些适应电流如何改变体内类似的波动突触输入、尖峰率输出和尖峰序列统计之间的关系，还不是很清楚。在这项计算研究中，我们表明，主要依赖于亚阈膜电压的适应电流会以减法的方式改变神经元的输入-输出关系（I-O 曲线），从而增加响应阈值，并降低其在低尖峰率时的斜率（响应增益）。一个依赖于尖峰的适应电流以除法的方式改变 I-O 曲线，从而降低响应增益。这两种适应电流都会自然地增加平均尖峰间间隔（ISI），但它们对 ISI 变异性的影响方式相反。亚阈电流总是会增加变异性，而尖峰触发电流则会降低由波动主导的输入引起的高变异性，并在平均输入较大时增加低变异性。这些对 I-O 曲线的影响与具有异步不规则活动的网络中的突触抑制所引起的影响相匹配，对于后者，我们发现外部和递归抑制分别引起了减法和除法变化。然而，突触抑制总是会增加 ISI 变异性。我们推导出了 I-O 曲线和 ISI 变异性的解析表达式，这些表达式证明了我们的结果的稳健性。此外，我们展示了慢 K(+)电导的生物物理参数如何导致两种不同类型的适应电流，并发现 Ca(2+)-激活的 K(+)电流可以被简单的尖峰依赖描述有效地捕捉，而毒蕈碱敏感或 Na(+)激活的 K(+)电流则表现出主要的亚阈成分。
