Data from: Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

漏积分-发放（Leaky integrate-and-fire, LIF）神经网络模型常被用于研究神经网络的尖峰动力学如何随刺激、任务或动态网络状态发生变化。然而，在体神经生理学研究通常更多通过局部场电位（local field potential, LFP）来测量神经元微环路的整体活动。鉴于局部场电位由神经元膜上空间分离的电流所产生，因此无法直接从点状LIF神经元模型定义的物理量中计算得到。本研究探索了基于点状神经元LIF网络标准输出预测局部场电位的最优近似方案。为寻找到这一最佳“LFP代理变量（LFP proxy）”，我们将基于LIF网络输出（例如放电速率、膜电位、突触电流）的候选代理变量所得到的LFP预测结果，与将LIF网络突触输入电流注入具有真实形态、胞体与突触空间分布的多室神经元三维（3D）网络模型时所获得的“真实值（ground-truth）”LFP进行了对比。我们发现，对LIF突触电流进行特定的固定线性组合，可以得到精准的LFP代理变量，其能够解释三维网络中所有记录位置处观测到的LFP时间序列的大部分方差。该代理变量在多种条件下均表现优异，包括神经元形态的大幅变化。本研究结果提供了一种简单公式，可在单个锥体神经元集群主导LFP生成的场景下，从LIF网络模拟中估算LFP的时间序列，从而助力计算模型与在体实验LFP记录之间的定量对比研究。