连接各向异性在培养神经元网络动力学中的作用
Role of connectivity anisotropies in the dynamics of cultured neuronal networks
AI总结：
本文结合实验数据与 Izhikevich 模型构建数值模型，研究工程化体外神经元网络中的连接各向异性、噪声和轴突长度对活动模式及结构连接重建的影响，为芯片大脑设计提供预测工具。
摘要：
近年来，实验室培育的工程化体外活体神经元网络已成为一种实验技术，用于理解神经元集群的集体行为与其底层连接之间的关系。设计此类工程系统时的一个固有障碍是，难以预测涌现网络的动态库及其对实验变量的依赖。为弥补这一空白，并受近期实验研究启发，我们在此提出一个数值模型，其目标首先是复现通过工程手段印刻的连接各向异性，随后再现神经元网络的集体行为并作出预测。我们将实验测量的、具有生物学真实性的数据与 Izhikevich 模型相结合，以量化神经元网络相对于可调结构参数和动力学参数的动力学。这些参数包括突触噪声、印刻各向异性的强度以及平均轴突长度。平均轴突长度参与体外神经元发育过程的模拟。我们表明，该模型能够捕捉工程化神经元培养物的行为：在这些培养物中会涌现出丰富的活动模式库，但其细节强烈依赖于连接细节和噪声。结果还表明，连接各向异性的存在显著提高了从活动数据重建结构连接的能力；在探索神经元网络中结构与功能关系的过程中，这一方面十分重要。我们的工作提供了计算机模拟基础，可协助实验人员设计实验室体外网络并预测其结果；在构建可靠的芯片大脑电路以及探索输入-输出关系或信息编码等关键方面的努力中，这一点尤为重要。
英文摘要：
Laboratory-grown, engineered living neuronal networks in vitro have emerged in the last years as an experimental technique to understand the collective behavior of neuronal assemblies in relation to their underlying connectivity. An inherent obstacle in the design of such engineered systems is the difficulty to predict the dynamic repertoire of the emerging network and its dependence on experimental variables. To fill this gap, and inspired on recent experimental studies, here we present a numerical model that aims at, first, replicating the anisotropies in connectivity imprinted through engineering, to next realize the collective behavior of the neuronal network and make predictions. We use experimentally measured, biologically-realistic data combined with the Izhikevich model to quantify the dynamics of the neuronal network in relation to tunable structural and dynamical parameters. These parameters include the synaptic noise, strength of the imprinted anisotropies, and average axon lengths. The latter are involved in the simulation of the development of neurons in vitro. We show that the model captures the behavior of engineered neuronal cultures, in which a rich repertoire of activity patterns emerge but whose details are strongly dependent on connectivity details and noise. Results also show that the presence of connectivity anisotropies substantially improves the capacity of reconstructing structural connectivity from activity data, an aspect that is important in the quest for understanding the structure-to-function relationship in neuronal networks. Our work provides the in silico basis to assist experimentalists in the design of laboratory in vitro networks and anticipate their outcome, an aspect that is particularly important in the effort to conceive reliable brain-on-a-chip circuits and explore key aspects such as input-output relationships or information coding.
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