Being able to predict the evolution of plastic deformation at the microstructural scale is of paramount importance in the engineering of materials for advanced applications. However, this is not straightforward because of the multiscale nature of deformation heterogeneity, both in space and time . The present thesis combines four related studies in a coherent work, which is aimed to develop experimental methods for studying crystal plasticity at the micro and mesoscale. A novel methodology for gold remodelling is initially proposed and used to apply high-density speckle patterns on the surface of stainless steel specimens. The unique proprieties of the speckle pattern enabled plastic deformation mapping with submicron resolution using digital image correlation (HDIC). It was therefore possible to study the concomitant evolution of microbands and transgranular deformation bands in such alloy. High-resolution deformation mapping also enabled comparison with high-resolution electron backscatter diffraction (EBSD) observations. The only partial correspondence of results proved the limits of EBSD in characterizing plastic deformation. The cause of such limitation is later identified in the reduced sensitivity to lattice slip of the EBSD technique. Hence, a novel method of HDIC data analysis is proposed to separate the contributions of lattice slip and lattice rotation from the deformation mapping. The method is adopted to characterize plasticity in austenitic stainless steel and at the plastic deformation zone (PDZ) around a silicon particle embedded in a softer aluminum matrix. Results show that the proposed experimental methodology has the unique capability of providing a complete description of the micro and mesoscale mechanics of crystal plasticity. HDIC therefore emerges as a key technique in the development of accurate physical-based multiscale crystal plasticity models.

<p>Over the past two decade, there have been renewed interests in the use of continuum models of dislocation to predict the plastic strength of metals from basic properties of dislocations. Such interests have been motivated by the unique self-organized dislocation microstructures that develop during plastic deformation of metals and the need to understand their origin and connection with strength of metals. This thesis effort focuses on the theoretical development of a vector-density based representation of dislocation dynamics on the mesoscale accounting for the kinematics of finite deformation. This model consists of two parts, the first is the development of the transport-reaction equations governing dislocation dynamics within the finite deformation setting, and the second focuses on the computational solution of the resulting model. The transport-reaction equations come in the form of a set of hyperbolic curl type transport equations, with reaction terms that nonlinearly couple these equations. The equations are also geometrically non-linear due to finite deformation kinematics and by their constitutive closure. The solution of the resulting model consists of two parts that are coupled in a staggered fashion, the crystal mechanics equations are lumped in the stress equilibrium equations, and the dislocation transport-reactions equations. The two sets of equations are solved by the Galerkin and First-Order System Least-Squares (FOSLS) finite element methods. A special attention is given to the accurate modeling of glissile dislocation junctions using de Rahm currents and graph theory ideas. The introduction of these measures requires the derivation of further transport relations. Using homogenization theory, we specialize the proposed model to a mean deformation gradient driven bulk plasticity model. Lastly, we simulate bulk plasticity behavior and compare our results against experiments.</p>

No description available.

Contemporary cellular models of learning and memory are articulated around the idea that synapses undergo activity-dependent weight changes. However, conventional forms of Hebbian plasticity do not adequately address certain features inherent to behavioral learning. First, associative learning driven by delayed behavioral outcomes introduces a temporal credit assignment problem, whereby one must remember which action corresponds to which outcome. Yet, current models of associative synaptic plasticity, such as spike-timing-dependent plasticity, require near coincident activation of pre- and postsynaptic neurons (i.e., within ~ 10 ms), a time delay that is orders of magnitude smaller than that required for behavioral associations. For individual neurons to associate two cues, a biological mechanism capable of potentiating synaptic weights must be able to bind events that are separated in time. Theoretical work has suggested that a synaptic eligibility trace, a time-limited process that momentarily renders synapses eligible for weight updates via delayed instructive signals, can solve this problem. However, no material substrate of eligibility traces has been identified in the brain. Second, under certain conditions, neurons need to swiftly update their weights to reflect rapid learning. Current plasticity experiments require the repetition of multiple pairings to induce long-term synaptic plasticity. In this thesis, I addressed these problems using a combination of whole-cell recordings, two-photon uncaging, calcium imaging, and mechanistic modeling. I uncovered a form of synaptic plasticity known as behavioral timescale synaptic plasticity (BTSP) in layer 5 pyramidal neurons in the prefrontal cortex of mice. BTSP induced synaptic potentiation by pairing temporally separated pre- and postsynaptic events (0.5 s - 1 s), regardless of their order. The temporal window for BTSP induction offers a line of solution to the temporal credit assignment problem by highlighting the presence of a synaptic mechanism that expands the time for the induction of activity-dependent long-term synaptic plasticity, spanning hundreds of milliseconds. We further found that BTSP can be induced following a single pairing, enabling rapid weight updates required for one-shot learning. Using two-photon calcium imaging in apical oblique dendrites, I discovered a novel short-term and associative plasticity of calcium dynamics (STAPCD) that exhibited temporal characteristics mirroring the induction rules of BTSP. I identified a core set of molecular components crucial for both STAPCD and BTSP and developed a computational simulation that models the calcium dynamics as a latent memory trace of neural activity (i.e., eligibility traces). Together, we find that calcium handling by the endoplasmic reticulum enables synaptic weight updates upon receipt of delayed instructive signals, obeys rules of burst-dependent one-shot learning, and thus provides a mechanism that satisfies the requirements anticipated of eligibility traces. Collectively, these findings offer a neural mechanism for the binding of cellular events occurring in single shot and separated by behaviorally relevant temporal delays to induce potentiation at synapses, providing a cellular model of associative learning.

<p>Tinnitus is an auditory disorder characterized by the perception of a ringing, hissing or buzzing sound with no external stimulus. Because the most common cause of chronic tinnitus is hearing loss, this neurological disorder is becoming increasingly prevalent in our noise-exposed and ageing society. With no cure and a lack of effective treatments, there is a need for a comprehensive understanding of the neural underpinnings of tinnitus. This dissertation outlines the development and validation of a comprehensive theoretical model of cortical correlates of tinnitus that is used to shed light on the development of tinnitus and to propose improvements to tinnitus treatment strategies.</p> <p>The first study involved the development of a computational model that predicts how homeostatic plasticity acting in the auditory cortex responds to hearing loss. A subsequent empirical study validated a more biologically plausible version of this computational model. The goal of these studies was to determine whether and how a form of plasticity that maintains balance in neural circuits can lead to aberrant activity in the auditory cortex. The final study extends the validated computational model to develop a comprehensive theoretical framework characterizing the potential role of homeostatic and Hebbian plasticity in the development of most major cortical correlates of tinnitus.</p> <p>These theoretical and empirical studies provide a novel and complete description of how neural plasticity in adult auditory cortex can respond to hearing loss and result in the development of tinnitus correlates.</p> / Doctor of Philosophy (PhD)

No description available.

A new type of computer-controlled instrument has been developed to measure microviscoelastic properties of thin materials. It can independently control and measure indentation loads and depths in situ revealing information about material creep and relaxation. Sample and indenter positions are measured with a specially designed polarization interferometer. Indenter loadings can be varied between 0.5 and 10 grams and held constant to ±41 mg. The resulting indentation depths can be measured in situ to ±1.2 nm. The load required to maintain constant indentation depths from 0.1 to 5.0 microns can be measured in situ to ±3.3 mg and the depth held constant to ±15 nm.

The mitral cells of the accessory olfactory bulb (AOB) of anuran frogs project their axons directly to the medial amygdala (MeA) along the accessory olfactory tract. An en bloc preparation of the telencephalon of the American bullfrog Lithobates catesbeiana was utilized to study a form of low-frequency inducible long-term potentiation (LTP) expressed at the synapse formed between the terminals of the accessory olfactory tract and the neurons of the MeA. Delivery of repetitive 1Hz-stimulation or sets of 5Hz tetani to the accessory olfactory tract both induced potentiation that was stable for over an hour, as measured by extracellular field recordings. LTP induced by 5Hz tetanus was associated with a decrease in paired-pulse ratio, which would be consistent with an increased probability of release contributing to the increased synaptic strength. Blockade of neither NMDA nor kainate glutamate receptors, with AP5 and UBP310 respectively, prevented LTP induction by 5Hz tetanus; however expression of LTP was partially masked in the presence of UBP310. These results suggest that kainate receptors are involved in the expression of LTP at the AOB-MeA synapse, though the means by which LTP is induced remains unclear. / Graduate / 2016-09-28

Small but mighty phytoplankton can be used as excellent model organisms to answer questions that are of importance to marine biologists and researchers in experimental evolution alike. For example, marine biologists are interested in finding out, how, in a changing ocean, the phytoplankton foundation of the ocean ecosystem is going to change - can we use short-term data to extrapolate to longer timescales? What are the physiological consequences of selection in stable and fluctuating high-pCO2 environments? From a more evolutionary perspective, is elevated pCO2 alone enough to drive evolution in marine algae? Can we select organisms to maintain plasticity in fluctuating environments, and how does selection in a fluctuating environment affect their ability to evolve? Can we detect a cost of plasticity? I have used theoretical and practical approaches from both disciplines to answer these questions, as they are ultimately similar questions that are important to address, and the lack of communication between disciplines has lead to conflicting predictions on the fate of populations in changing environments. Using evolutionary theory and applying it to an organism with a known function in the marine environment allows us to make ecologically relevant predictions while also enabling us to disentangle the underlying evolutionary mechanisms. While there have been some studies focusing on evolution of marine algae in climate change scenarios since I started my PhD, my study is the first to test the link between phenotypic plasticity and adaptation empirically, and it is also the first to use 16 rather than single or few genotypes of an algae, thereby creating the statistical power necessary to make any predictions. In a short-term CO2 enrichment study, and a selection experiment, those 16 physiologically and genetically distinct lineages of Ostreococcus, the smallest free living eukaryote, were selected for 400 generations in fluctuating and stable high pCO2 environments. I have shown that short-term plastic responses in phenotype can predict the magnitude of long-term evolutionary ones. Ostreococcus lineages in fluctuating environments evolve to be more plastic with no associated costs, and the adaptive response to selection in a high pCO2 environment is to grow more slowly in monoculture, but to be more successful competitors in mixed culture. High-pCO2 evolved lineages are genetically and physiologically different from their ancestors. Importantly, their quality as a food source for zooplankton will change, with possible repercussions for the ocean ecosystem at a whole. Furthermore, the lineages’ ability to perceive pCO2 levels in the surrounding medium is altered after evolution in fluctuating and high pCO2 environment, allowing them to broaden the window in which they can respond to changes in their environment without suffering metabolic stress.

The objective of this work is to provide various improvements to the modeling and uncertainty quantification of fatigue lives of materials as understood via simulation of crystal plasticity models applied to synthetically reconstructed microstructures. A computational framework has been developed to automate standardized analysis of crystal plasticity models in the high cycle fatigue regime. This framework incorporates synthetic microstructure generation, simulation preparation, execution and post-processing to analysis statistical distributions related to fatigue properties. Additionally, an improved crack nucleation and propagation approach has been applied to Al 7075-T6 to improve predictive capabilities of the crystal plasticity model for fatigue in various loading regimes. Finally, sensitivities of fatigue response to simulation and synthetic microstructure properties have been explored to provide future guidance for the study of fatigue quantification based on crystal plasticity models.