Information processing by respiratory motoneurons

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Understanding how neurons transform input into output, i.e. how they integrate and process information, is fundamental to understanding brain function and how behaviour emerges from the activity of neural networks. The geometry of the dendritic tree has long been recognized as a critical factor in determining how synaptic input is transformed into neuronal output, but how the brain utilizes the complex geometries of neurons and their dendritic trees to achieve this function remains unclear. In this study we will use optical and electrophysiological methods (that allow us to record from single neurons) to map the information processing tree (dendrites) of motoneurons that drive respiratory muscles. These methods will be applied to brain slices from neonatal rodents that retain a functioning neural network that generates rhythmic "breathing" in a dish. This endogenous behaviour is critical. It means that the synapses mediating inspiratory drive to motoneurons will be activated in a manner similar to what happens normally; i.e. we will monitor motoneuron activity while the inspiratory synaptic input is physiologically processed by the dendritic tree. Our optical mapping will examine anatomical structure as well as functional behaviour. By using our new methods for cellular microsurgery we will modify the dendrite structure of the neuron to clarify the role of different dendrite branches in processing inspiratory inputs (i.e., do all dendrites have a similar role?). We will also explore how the electrical properties of the dendrites and inputs from modulatory neurons important in controlling brain state are organized spatially on the dendrites, how they change dendritic properties and how these changes influence integration of inspiratory inputs. These experiments will provide new insight into neuronal integration and help clarify the basis for how the brain achieves such remarkable information processing.