The field of neuroscience has recently seen optogenetics emerge as a highly utilized and powerful method of non-invasive neural activation and inhibition. This thesis seeks to enhance the optogenetic toolbox through the design, construction, and evaluation of a number of hardware and software modules for research in Caenorhabditis elegans neuroscience. In the first aim, we combine optogenetics, microfluidics, and automated image processing, to create a system capable of high-throughput analysis of synaptic function. In the second aim, we develop a multi-modal illumination system for the manipulation of optogenetic reagents. The system is capable of multi-spectral illumination in definable patterns, with the ability to dynamically alter the intensity, color, and shape of the illumination. The illumination system is controlled by a set of software programs introduced in aim three, and is demonstrated through a set of experiments in aim four where we selectively activate and inhibit specific neural nodes expressing optogenetic reagents in freely moving C. elegans. With the ability to target specific nodes in a freely moving animal, we can correlate specific neural states to behaviors allowing for the dissection of neural circuits. Taken together, the developed technologies for optogenetic researchers will allow for experimentation with previously unattainable speed, precision and flexibility.

Population-wide synchronized rhythmic bursts of electrical activity are present in a variety of neural circuits. The proposed general mechanisms for rhythmogenesis are often attributed to intrinsic and synaptic properties. For example, the recurrent excitation through excitatory synaptic connections determines burst initiation, and the slower kinetics of ionic currents or synaptic depression results in burst termination. In such theories, a slow recovery process is essential for the slow dynamics associated with bursting. This thesis presents a new hypothesis that depends on the connectivity pattern among neurons rather than a slow kinetic process to achieve the network-wide bursting. The thesis begins with an introduction of bursts of electrical activity in a purely excitatory neural network and existing theories explaining this phenomenon. It then covers the small-world approach, which is applied to modify the network structure in the simulation, and the Morris-Lecar (ML) neuron model, which is used as the component cells in the network. Simulation results of the dependence of bursting activity on network connectivity, as well as the inherent network properties explaining this dependence are described. This work shows that the network-wide bursting activity emerges in the small-world network regime but not in the regular or random networks, and this small-world bursting primarily results from the uniform random distribution of long-range connections in the network, as well as the unique dynamics in the ML model. Both attributes foster progressive synchronization in firing activity throughout the network during a burst, and this synchronization may terminate a burst in the absence of an obvious slow recovery process. The thesis concludes with possible future work.

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2004. / Vita. Includes bibliographical references (leaves 109-114).

No description available.

There is a significant amount of computational literature on networks of neurons and their resulting behavior. This dissertation combines electrophysiology experiments with computational modeling to validate the assumptions and results found in this literature. First, we investigate the weak coupling assumption, which states that the phase response of a neuron to weak stimuli is separable from the stimulus waveform. For weak stimuli, there is an intrinsic neuronal property described by the infinitesimal phase response curve (IPRC) that will predict the phase response when convolved with the stimulus waveform. Here, we show that there is a linear relationship between the stimulus and phase response of the neuron, and that we are able to obtain IPRCs that successfully predict the neuronal phase response. Next, we use hybrid networks of neurons to study the phase locking behavior of networks as the synaptic time constant is changed. We verify that networks show anti-phase synchrony for fast time constants, and in-phase synchrony for slow time constants. We also show that phase models and phase response curves (PRCs) qualitatively predict phase locking observed in electrophysiology experiments. Finally, we investigate the stability of the dynamic clamp system. We determined that the maximal conductance of the current being simulated, the dynamic clamp sampling rate, the amount of electrode resistance compensation, and the amount of capacitance compensation all affect when the instability is present. There is a dramatic increase in stability when the electrode resistance and system capacitance are well compensated.

Thesis (Ph. D.)--University of California, San Diego, 1999. / Vita. Includes bibliographical references (leaves 86-90).

Mémoire (M.Eng.)--Université du Québec à Chicoutimi, 1997. / Document électronique également accessible en format PDF. CaQCU

Thèse (M.Eng.) -- Université du Québec à Chicoutimi, 2004. / Bibliogr.: f. [139]-140. Document électronique également accessible en format PDF. CaQCU

Thesis (Ph. D.)--University of California, San Diego, and San Diego State University, 2005. / Vita. Includes bibliographical references (leaves 160-178).

Thesis (Ph. D.)--University of California, San Diego, 2008. / Title from first page of PDF file (viewed June 1, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 124-131).