Dynamic network reconfiguration at the transition between motor programs

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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.