Department of Data Science, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
PLoS Comput Biol. 2022 Aug 12;18(8):e1010353. doi: 10.1371/journal.pcbi.1010353. eCollection 2022 Aug.
Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ('spikes') or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.
在现代神经科学中，不同细节水平的神经活动模拟无处不在，有助于解释细胞和回路水平的实验数据和潜在神经机制。反映整个神经组织跨膜电流的脑信号的细胞外测量仍然很常见。测量信号的较低频率（≲300Hz）通常源于神经群体之间的递归相互作用驱动的突触活动，计算模型也应纳入对这种信号的准确预测。由于计算资源有限，大规模神经元网络模型（≳106 个神经元左右）通常需要降低生物物理细节水平，主要考虑动作电位（“尖峰”）或尖峰率的时间。因此，相应的细胞外信号预测未能很好地解释其生物物理起源。在这里，我们提出了一种计算框架，用于预测源自突触活动的这种细胞外信号的时空滤波器核，该框架考虑了神经元、群体和递归连接的生物物理特性。通过为每个连接路径的群体尖峰率卷积适当的核并求和来获得信号。我们的主要结果是，通过线性化突触和膜动力学、细胞分布、传导延迟和容积导体模型得出的核，可以准确捕捉基于导纳的多腔神经元网络的地面真实细胞外信号的时空动态。一个特别的观察结果是，必须考虑由持久突触激活引起的有效膜时间常数的变化。这项工作也是在计算效率方面的重大进展，从大规模尖峰和基于率的神经元网络模型准确地预测基于生物物理的信号，与生物物理详细的网络模型相比，大大减少了信号预测时间。这项工作还深入了解了实验记录的神经元活动的低频细胞外信号如何可能近似线性地依赖于尖峰活动。一个新的软件工具 LFPykernels 作为该框架的参考实现。
