Dynamic network reconfiguration at the transition between motor programs

一个美丽的芭蕾舞演员舞蹈可以被认为是一个连续的运动动作（行为）链，如跳跃，向前跑，向后跑等神经生物学实验表明，每一个特定的运动行为可以通过一组神经元产生的电活动模式来表征。然而，对不同模式之间的转换知之甚少。神经元相互连接形成神经元网络，但尚不清楚网络如何从一种行为切换到另一种行为。当舞蹈中的向前跑切换到向后跑时，神经元网络中会发生什么？在这个项目中，我们将研究年轻的青蛙蝌蚪只有两个有节奏的运动行为：向前游泳和向后挣扎。在游泳时，两侧神经元活动的交替导致肌肉收缩的快速波从头到尾传播。如果被捕食者抓住，或者遇到障碍物，蝌蚪必须迅速逃跑。在这种情况下，它会迅速切换到挣扎的行为，在此期间，速度较慢，但更强的波从尾部传播到头部，导致强大的向后运动，这可能对生存至关重要。我们特别感兴趣的是，产生挣扎的神经网络与产生游泳的神经网络是相同的。当蝌蚪被捕食者捕获时，其皮肤上的持续感觉导致额外的神经细胞群被激活，而其他神经细胞群被关闭。这意味着网络会自动重新配置自己，以产生不同的行为，从而可能帮助动物逃跑。这种产生不同行为的快速重新配置也发生在高等脊椎动物和人类更复杂的大脑网络中。神经科学家正在设计新的光学成像方法来监测这些复杂系统中神经细胞群的活动。然而，在蝌蚪中，我们可以直接记录成对的单个神经细胞，以精确测量它们的活动和它们交换的信息在游泳和挣扎之间的转变中是如何改变的。因此，蝌蚪提供了一种无与伦比的能力来定义和理解神经网络重构过程中发生的事情。大多数基本的神经元机制在脊椎动物物种中高度保守。从蝌蚪身上得到的结果将对进一步了解哺乳动物更复杂的大脑网络非常有用。我们可以在蝌蚪身上进行的极其详细的记录也将使我们能够建立涉及其运动控制的蝌蚪神经元网络的详细计算机模型。这些模型将被用来研究网络操作的影响，这些影响是不可能通过实验研究的，并产生重要的见解。通过这种方式，模型可以制定新的假设，这些假设可以反过来通过实验进行测试。使用这种模型和生理记录的组合，我们将理解1）为什么挣扎波比游泳波更强大; 2）为什么它们从尾巴传播到头部，与游泳中的头到尾传播相反;以及3）神经元回路如何改变自己以产生这些不同的行为。这一发现的意义将超越基础神经科学研究。例如，这些原理可以用来更好地设计需要在困难环境中导航而不会卡住的机器人。

A beautiful ballerina dance can be considered as a continuous chain of motor actions (behaviours) such as jump, forward run, backward run, etc. Neurobiological experiments show that each particular motor behaviour can be characterised by a set of neurons producing a pattern of electrical activity. However, transitions between different patterns are poorly understood. Neurons are interconnected to form a neuronal network but it is not clear how the network can switch from one behaviour to another. What happens in the neuronal network when the forward run in a dance switches to backward? In this project, we will study young frog tadpoles with just two rhythmic motor behaviours: forward swimming and backward struggling. In swimming, alternation in neuron activity on each side leads to rapid waves of muscle contraction propagating from head to tail. If held by a predator, or stuck against an obstacle, the tadpole must quickly escape. In that case, it rapidly switches to the struggling behaviour, during which slower, but more stronger waves propagate from tail to head, leading to a powerful backward movement, which could be critical for survival. Of particular interest to us, is that the neuronal network that produce struggling is the same network that produces swimming. When the tadpole is captured by a predator, the continuous sensation on its skin results in the activation of extra groups of nerve cells while other groups of nerve cells are turned off. This means that the network reconfigures itself automatically to generate a different behaviour, potentially assisting the animal to escape. This fast reconfiguration to produce a different behaviour also occurs in more sophisticated brain networks in higher vertebrates and humans. Neuroscientists are devising new optical imaging methods to monitor the activity of groups of nerve cells in these complicated systems. In the tadpole, however, we can directly record from individual nerve cells in pairs, to measure precisely how their activity and the messages they exchange are altered at the transition between swimming and struggling. Thus, the tadpole provides an unparalleled ability to define and understand exactly what happens during neuronal network reconfiguration. Most fundamental neuronal mechanisms are highly conserved across vertebrate species. The results from tadpoles will be immensely useful to further understanding of more complex brain networks in mammals.The extremely detailed recordings that we can perform on the tadpole will also allow us to build detailed computer models of tadpole neuronal network involved in its motor control. These models will be used to examine the effects of manipulations of the network that are impossible to study experimentally and generate important insights. This way, the models can formulate new hypotheses that may be in turn tested experimentally. Using this combination of models and physiological recordings, we will understand 1) why the struggling waves are more powerful than the swimming waves; 2) why they propagate from tail to head, contrary to the head-to-tail propagation in swimming; and 3) how the neuronal circuit changes itself to produce these distinct behaviours. The findings will have implications beyond basic neuroscience research. For example, the principles could be used to better design robots that need to navigate difficult environments without getting stuck.