Thesis (Ph.D.)--Ohio University, June, 2005. / Title from PDF t.p. Includes bibliographical references (p. 130-137)

Thesis (Ph.D.)--University of Oregon, 2005. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 96-103). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Michaels, Thomas E., Committee Member ; LaPlaca, Michelle, Committee Member ; Frazier, A. Bruno, Committee Member ; DeWeerth, Stephen P., Committee Member ; Allen, Mark G., Committee Chair.

Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (leaves 56-67). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Drosophila larval crawling is an attractive system to study patterned motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors including head casts, turning, and feeding. It is likely that some neurons are used in all these behaviors (e.g. motor neurons), but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We defined 10 phenotypic categories that could uniquely be evoked upon stimulation, and provide further in depth analysis of two of these categories to understand the origins of the evoked behaviors. We first identified the evolutionarily conserved Even-skipped+ interneuron phenotype (Eve/Evx). Activation or ablation of Eve+ interneurons disrupted bilaterally symmetric muscle contraction amplitude, without affecting left-right synchronous timing. TEM reconstruction places the Eve+ interneurons at the heart of a sensorimotor circuit capable of detecting and modifying body wall muscle contraction We then went on to identify a unique pair of descending neurons dubbed the ‘Mooncrawler’ descending neurons (McDNs) to be sufficient to generate reverse locomotion. We show that the McDNs are present at larval hatching, function during larval life, and are remodeled during metamorphosis while maintaining basic morphological features and neural functions necessary to generate backwards locomotion. Finally, using serial section Transmission Electron Microscopy (ssTEM) to map neural connections to upstream and downstream elements provides a mechanistic view of how sensory information is received by the McDNs and transmitted to the VNC motor system to perform backwards locomotion. Finally, we show that these McDNs are the same as those identified in recent work in Drosophila adults (Bidaye et al. 2014) to be sufficient to generate reverse locomotion. This dissertation includes previously published, co-authored material.

Computational modelling is playing an increasing role in neuroscience research by providing not only theoretical frameworks for describing the activity of the brain and the nervous system, but also by providing a set of tools and techniques for better understanding data obtained using various recording techniques. The focus of this thesis was on the latter - using computational modelling to assist with analyzing measurement results and the underlying mechanisms behind them. The first study described in this thesis is an example of the use of a computational model in the case of intracellular in vivo recordings. Intracellular recordings of neurons in vivo are becoming routine, yielding insights into the rich sub-threshold neural dynamics and the integration of information by neurons under realistic situations. In particular, these methods have been used to estimate the global excitatory and inhibitory synaptic conductances experienced by the soma. I first present a method to estimate the effective somatic excitatory and inhibitory conductances as well as their rate and event size from the intracellular in vivo recordings. The method was applied to intracellular recordings from primary motor cortex of awake behaving mice. Next, I studied how dendritic filtering leads to misestimation of the global excitatory and inhibitory conductances. Using analytical treatment of a simplified model and numerical simulations of a detailed compartmental model, I show how much both the mean, as well as the variation of the synaptic conductances are underestimated by the methods based on recordings at the soma. The influence of the synaptic distance from the soma on the estimation for both excitatory as well as inhibitory inputs for different realistic neuronal morphologies is discussed. The last study was an attempt to classify the synaptic location region based on the measurements of the excitatory postsynaptic potential at two different locations on the dendritic tree. The measurements were obtained from the in vitro intercellular recordings in slices of the somatosensory cortex of rats when exposed to glutamate uncaging stimulation. The models were used to train the classifier and to demonstrate the extent to which the automatic classification agrees with manual classification performed by the experimenter.

Neurons transmit information through spikes in neural network through synaptic couplings. Given the prevalence of correlation among neural spike trains experimentally observed in different brain areas, it is of interest to study how neurons compute correlated input. Yet how it depends on the synaptic properties and conductance kinetics in neuronal interaction is very little known. Through simulation of leaky integrate-and-fire (LIF) neurons, we have studied the effects of excitatory and inhibitory synaptic decay times, level of background activities and higher-order conductance effects on the output correlation of different time scales for neurons receiving correlated excitatory input, and provided important understanding on the mechanism of how these factors influence neural computation of such correlated input. We showed that when the conductance effects are totally ignored, increasing excitatory synaptic decay time jitters output spike time and shapes the output correlation of short to medium time scale, while the output correlation of very long time scale is determined by the membrane time constant. When conductance effects are considered, this is no longer the case as the effective membrane time constant becomes comparable to the excitatory decay time. We found that the ratio of long-term correlation to short-term correlation (synchrony) increases with excitatory synaptic decay time and decreases with the level of input activities due to the combined effects of jittered spike time, which can be predicted from the time window and magnitude of the effects of a single input spike on membrane potential, and burst firing. In particular, it is possible for neurons with small excitatory synaptic decay time in high conductance state to respond to correlated input by solely giving extra precisely timed synchronous spikes without exhibiting correlation of longer time scale. In addition, we found that inhibitory synaptic decay time shapes correlation by controlling the relative contribution of excitatory and inhibitory input to output firing. As a result, both output correlation and synchrony increase with it. These results are qualitatively true for a wide range of input correlation and synaptic efficacies. Finally, we showed that fluctuations of conductance and membrane potential reduce output correlation, which can be explained by the reduced prevalence of burst firing. These results suggest that spike initiation dynamics of neurons can be well characterized by their synaptic decay times and the level of input activities. These properties are therefore expected to influence neurons’ ability to code temporal information. These results also hint that correlation, in particular that of long time scale, would be lower if more realistic biophysical features like neural adaptations and network circuitry with feed-forward or recurrent inhibition are considered. It suggests that studies using single LIF neurons tend to overestimate output correlation and underestimate the ability of neurons in producing precisely timed output.

Cell patterning platforms support diverse research goals including tissue engineering, the study of cell physiology, and the development of biosensors. Patterning and interfacing with neurons is a particular challenge, being approached via various bioengineering approaches. Such constructs, when optimised, can inform our understanding of neuronal computation and learning, and ultimately aid the development of intelligent neuroprostheses. A fundamental pre-requisite is the ability to dictate the spatial organization and topography of patterned neuronal cells. This thesis details efforts to pattern neurons using photolithographically defined arrays of the polymer parylene-C, printed upon oxidised silicon wafers. Initial work focused on exploring the parylene-C:SiO2 construct as a wide-ranging cell-patterning platform, assessing cell adhesion from both substrate- and cell-centric perspectives. Next, the LUHMES (Lund Human Mesencephalic) cell line was used to explore the potential for construction of interrogatable, topographically-defined neuronal networks. In isolation, LUHMES neurons failed to pattern and did not show any morphological signs of cellular differentiation. However, in the context of a cellular template (the HEK 293 cell line which was found to pattern reliably), LUHMES were able to adhere secondarily on-chip. This co-culture environment promoted morphological differentiation of neurons. As such, HEK 293 cells fulfilled a role analogous to glia, dictating neuronal cell adhesion and generating an environment conducive to neuronal survival. Neurites extended between islands of adherent cell somata. The geometry and configuration of parylene-C influenced the organisation of neurites. With appropriate designs, orthogonal neuronal networks could be created. The dominant guidance cue for neurite growth direction appears to be a diffusible chemotactic agent. HEK 293 cells were later replaced with slower growing human glioma-derived precursors, extracted during tumour debulking surgery. These primary cells patterned accurately on parylene-C and provided a similarly effective, and longer lasting, scaffold for neuronal adhesion.

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