UC San Diego

Characterizing the many physical properties of biological neurons has been historicallydifficult. The use of conductance models describing how properties of these systems change with time can be combined with measured voltage traces to help characterize immeasurable properties of a neuron. This is accomplished using data assimilation, which formulates the inference of these properties as probability maximization using nonlinear optimization. Because measurement noise and model error are inevitable in the study of complex systems, the methods used in this dissertation are designed to cope with unknown processes. This dissertation starts with an overview of data assimilation by formulating the problem xiii data assimilation attempts to solve. I then introduce two methods, nudging and variational annealing, which I use to characterize the properties of a neuron in the Zebra Finch songbird system, HVCX. A key result from this experiment is that there are statistical differences in estimated model parameters for neurons from different birds. Current artificial neural network models are unmanageably large (billions of parameters) and need massive amounts data and computational power to train. The insect olfactory system is a biological network which is capable of learning and identifying new odors quickly in the presence of interference. These properties make it attractive model to explore as a classifier for machine learning tasks. I give an overview of the insect olfactory system by describing its key properties. I use these properties to create a simplified classification system using a winnerless competition network as a pre-processor to a support vector machine. I demonstrate this networks classification capabilities using classes designed to resemble the stimulus an insect receives in the presence of odors. One key result from this experiment is that our network is capable of identifying a mixtures of previously seen odors.