Wybo Willem Am, Jordan Jakob, Ellenberger Benjamin, Marti Mengual Ulisses, Nevian Thomas, Senn Walter
Department of Physiology, University of Bern, Bern, Switzerland.
Elife. 2021 Jan 26;10:e60936. doi: 10.7554/eLife.60936.
Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models at any level of complexity. We show that (back-propagating) action potentials, Ca spikes, and -methyl-D-aspartate spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite. Furthermore, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide software that automatizes the simplification, eliminating a common hurdle toward including dendritic computations in network models.
树突塑造神经元中的信息流。然而，对于它们运作的空间复杂程度，人们几乎没有达成共识。通过精心选择在最小二乘意义上可求解的参数拟合，我们在任何复杂程度下都能获得准确的简化房室模型。我们表明，（反向传播的）动作电位、钙尖峰和N-甲基-D-天冬氨酸尖峰都可以用很少的房室来重现。我们还研究了传入空间连接基序是否可以通过切除目标分支并将受影响的突触分组到下一个近端树突上来简化。我们发现，如果时间电导波动保持在一个取决于切除分支与下一个近端树突之间输入电阻平均差异的极限以下，那么其余分支中的电压就能被重现。此外，我们的方法直接根据实验数据拟合简化模型，无需形态重建。我们提供了使简化自动化的软件，消除了将树突计算纳入网络模型的一个常见障碍。
