School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, United Kingdom, School of Computing and Mathematics, University of Plymouth, Plymouth, PL4 8AA, United Kingdom, and Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
J Neurosci. 2014 Jan 8;34(2):608-21. doi: 10.1523/JNEUROSCI.3248-13.2014.
How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting "network" is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental "rules," which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition.
脊椎动物神经系统轴突核心中的先驱网络最初是如何发展的？要理解任何全面的神经元网络，关键是要了解组成神经元、它们的特性、突触连接以及正常活动。我们的新策略利用基本的发育规则来生成模型网络，这些网络保留了单个神经元和突触的分辨率，并且能够重现正确的、整个动物的反应。我们将我们的发展策略应用于年轻的非洲爪蟾幼体，它们的脑干和脊髓共享一个核心脊椎动物计划，但复杂性可以控制。在对每种脊髓神经元进行详细的解剖和生理测量以完成描述性库之后，我们建立了由简单化学梯度和物理障碍控制的轴突生长模型。通过添加树突并允许突触连接的概率形成，我们重建了多达 2000 个神经元之间的网络连接。当由基于生理学特性的模型神经元和突触组成的“网络”被填充时，它可以通过模拟幼体游泳行为来响应感觉刺激。这个功能模型代表了对可以重现整个动物复杂、有节奏行为的脊椎动物神经网络的最完整重建。研究结果验证了我们用于生成具有单个神经元和突触分辨率的逼真网络的新的发展策略。我们使用它来演示早期的功能神经元连接和行为如何可能在生命中由于简单的发育“规则”而产生，这些规则为脊椎动物中枢神经系统提供了一个没有特定神经元到神经元识别的支架。
