摘要：

Effective movement is central to survival and it is essential for all animals to react in response to changes around them. In many animals the rhythmic signals that drive locomotion are generated intrinsically by small networks of neurons in the nervous system which can be switched on and off. In this thesis I use a very simple animal, in which the behaviours and neuronal networks have been well characterised experimentally, to explore the salient features of such networks. Two days after hatching, tadpoles of the frog Xenopus laevis respond to a brief touch to the head by starting to swim. The swimming rhythm is driven by a small population of electrically coupled brainstem neurons (called dINs) on each side of the tadpole. These neurons also receive synaptic input following head skin stimulation. I build biophysical computational models of these neurons based on experimental data in order to address questions about the effects of electrical coupling, synaptic feedback excitation and initiation pathways. My aim is better understanding of how swimming activity is initiated and sustained in the tadpole. I find that the electrical coupling between the dINs causes their firing properties to be modulated. This allows two experimental observations to be reconciled: that a dIN only fires a single action potential in response to step current injections but the population fires like pacemakers during swimming. I build on this hypothesis and show that long-lasting, excitatory feedback within the population of dINs allows rhythmic pacemaker activity to be sustained in one side of the nervous system. This activity can be switched on and off at short latency in response to biologically realistic synaptic input. I further investigate models of synaptic input from a defined swim initiation pathway and show that electrical coupling causes a population of dINs to be recruited to fire either as a group or not at all. This allows the animal to convert continuously varying sensory stimuli into a discrete decision. Finally I find that it is difficult to reliably start swimming-like activity in the tadpole model using simple, short-latency, symmetrical initiation pathways but that by using more complex, asymmetrical, neuronal-pathways to each side of the body, consistent with experimental observations, the initiation of swimming is more robust. Throughout this work, I make testable predictions about the population of brainstem neurons and also describe where more experimental data is needed. In order to manage the parameters and simulations, I present prototype libraries to build and manage these biophysical model networks.

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