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The cost of linearization

机译：线性化的成本

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Linear additivity of synaptic input is a pervasive assumption in computational neuroscience, and previously Bernander et al. (Journal of Neurophysiology 72:2743-2753, 1994) point out that the sublinear additivity of a passive neuronal model can be linearized with voltage-dependent currents. Here we re-examine this perspective in light of more recent findings and issues. Based on in vivo intracellular recordings, three voltage-dependent conductances seem to be of interest for pyramidal cells of the forebrain: two of them are amplifying, I_(NaP) and I_h; and one of them is attenuating, I_A. Based on particular I-V characteristics reported in the literature, each of these three voltage-dependent currents linearizes a particular range of synaptic excitation. Computational simulations use a steady-state, one-compartment model. They establish maximal linear ranges, where supralinear effects-due to adding too much of any one conductance-limit these ranges. Specific, carefully selected pairwise combinations of these currents can linearize larger ranges than either current alone. In terms of parameters, the steady-state I-V characteristics of each current are critical. On the other hand, the relationshipsrnbetween the results here and resting conductance to ground, synaptic conductance, and number of active synapses are simple and easily scaled; thus in regard to these three latter dependences, the results here are easily generalized. Finally, to improve our understanding of evolved function, the relative metabolic costs of linearization are quantified. In one case, there is a clear preference arising from this cost consideration (a particular I_h„ I_(NaP) pairing is less costly compared to a particular I_A, I_(NaP) pairing that produces an equivalent, linearized range). However in other cases, a preference will depend on the required range; but in any event, the largest linearized range observed here (28 mV), from a combination of I_h, and I_A, is significantly more costly than the 20 mV range that the I_h, I_(NaP) pair produces.
机译：突触输入的线性可加性在计算神经科学中是普遍的假设，以前是Bernander等人。 （Journal of Neurophysiology 72：2743-2753，1994）指出，被动神经元模型的亚线性可加性可以通过电压依赖性电流线性化。在这里，我们根据更多的最新发现和问题重新审视这种观点。基于体内细胞内记录，似乎对前脑的锥体细胞感兴趣的是三种电压依赖性电导：其中两种正在扩增，I_（NaP）和I_h；其中之一是衰减I_A。基于文献中报道的特定I-V特性，这三个电压相关电流中的每一个均使突触激发的特定范围线性化。计算仿真使用稳态的一室模型。它们建立了最大线性范围，其中由于添加过多的任何一种电导，超线性效应限制了这些范围。这些电流的特定的，精心选择的成对组合可以使比单个电流单独的更大的范围线性化。就参数而言，每个电流的稳态I-V特性至关重要。另一方面，这里的结果与对地面的静息电导率，突触电导和活动突触的数量之间的关系是简单的，并且容易缩放。因此，关于这后三个依赖关系，此处的结果很容易概括。最后，为了增进我们对进化功能的了解，对线性化的相对代谢成本进行了量化。在一种情况下，显然会因这种成本考虑而产生偏好（与产生等效的线性范围的特定I_A，I_（NaP）配对相比，特定的I_h„ I_（NaP）配对成本更低。）但是，在其他情况下，首选项将取决于所需的范围。但是无论如何，从I_h和I_A的组合来看，此处观察到的最大线性化范围（28 mV）比I_h，I_（NaP）对产生的20 mV范围要昂贵得多。

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