<p>It has long been appreciated that the process of learning might invoke a physical change in the brain, establishing a lasting trace of experience. Recent evidence has revealed that this change manifests, at least in part, by the formation of new connections between neurons, as well as the modification of preexisting ones. This so-called structural plasticity of neural circuits – their ability to physically change in response to experience – has remained fixed as a primary point of focus in the field of neuroscience. </p><p>A large portion of this effort has been directed towards the study of dendritic spines, small protrusions emanating from neuronal dendrites that constitute the majority of recipient sites of excitatory neuronal connections. The unique, mushroom-like morphology of these tiny structures has earned them considerable attention, with even the earliest observers suggesting that their unique shape affords important functional advantages that would not be possible if synapses were to directly contact dendrites. Importantly, dendritic spines can be formed, eliminated, or structurally modified in response to both neural activity as well as learning, suggesting that their organization reflects the experience of the neural network. As such, elucidating how these structures undergo such rearrangements is of critical importance to understanding both learning and memory. </p><p>As dendritic spines are principally composed of the cytoskeletal protein actin, their formation, elimination, and modification requires biochemical signaling networks that can remodel the actin cytoskeleton. As a result, significant effort has been placed into identifying and characterizing such signaling networks and how they are controlled during synaptic activity and learning. Such efforts have highlighted Rho family GTPases – binary signaling proteins central in controlling the dynamics of the actin cytoskeleton – as attractive targets for understanding how the structural modification of spines might be controlled by synaptic activity. While much has been revealed regarding the importance of the Rho GTPases for these processes, the specific spatial and temporal features of their signals that impart such structural changes remains unclear. </p><p>The central hypotheses of the following research dissertation are as follows: first, that synaptic activity rapidly initiates Rho GTPase signaling within single dendritic spines, serving as the core mechanism of dendritic spine structural plasticity. Next, that each of the Rho GTPases subsequently expresses a spatially distinct pattern of activation, with some signals remaining highly localized, and some becoming diffuse across a region of the nearby dendrite. The diffusive signals modify the plasticity induction threshold of nearby dendritic spines, and the spatially restricted signals serve to keep the expression of plasticity specific to those spines that receive synaptic input. This combination of differentially spatially regulated signals thus equips the neuronal dendrite with the ability to perform local biochemical computations, potentially establishing an organizational preference for the arrangement of dendritic spines along a dendrite. Finally, the consequences of the differential signal patterns also help to explain several seemingly disparate properties of one of the primary upstream activators of these proteins: brain-derived neurotrophic factor (BDNF). </p><p>The first section of this dissertation describes the characterization of the activity patterns of one of the Rho family GTPases, Rac1. Using a novel Förster Resonance Energy Transfer (FRET)- based biosensor in combination with two-photon fluorescence lifetime imaging (2pFLIM) and single-spine stimulation by two-photon glutamate uncaging, the activation profile and kinetics of Rac1 during synaptic stimulation were characterized. These experiments revealed that Rac1 conveys signals to both activated spines as well as nearby, unstimulated spines that are in close proximity to the target spine. Despite the diffusion of this structural signal, however, the structural modification associated with synaptic stimulation remained restricted to the stimulated spine. Thus, Rac1 activation is not sufficient to enlarge spines, but nonetheless likely confers some heretofore-unknown function to nearby synapses. </p><p>The next set of experiments set out to detail the upstream molecular mechanisms controlling Rac1 activation. First, it was found that Rac1 activation during sLTP depends on calcium through NMDA receptors and subsequent activation of CaMKII, suggesting that Rac1 activation in this context agrees with substantial evidence linking NMDAR-CaMKII signaling to LTP in the hippocampus. Next, in light of recent evidence linking structural plasticity to another potential upstream signaling complex, BDNF-TrkB, we explored the possibility that BDNF-TrkB signaling functioned in structural plasticity via Rac1 activation. To this end, we first explored the release kinetics of BDNF and the activation kinetics of TrkB using novel biosensors in conjunction with 2p glutamate uncaging. It was found that release of BDNF from single dendritic spines during sLTP induction activates TrkB on that same spine in an autocrine manner, and that this autocrine system was necessary for both sLTP and Rac1 activation. It was also found that BDNF-TrkB signaling controls the activity of another Rho GTPase, Cdc42, suggesting that this autocrine loop conveys both synapse-specific signals (through Cdc42) and heterosynaptic signals (through Rac1). </p><p>The next set of experiments detail one the potential consequences of heterosynaptic Rac1 signaling. The spread of Rac1 activity out of the stimulated spine was found to be necessary for lowering the plasticity threshold at nearby spines, a process known as synaptic crosstalk. This was also true for the Rho family GTPase, RhoA, which shows a similar diffusive activity pattern. Conversely, the activity of Cdc42, a Rho GTPase protein whose activity is highly restricted to stimulated spines, was required only for input-specific plasticity induction. Thus, the spreading of a subset of Rho GTPase signaling into nearby spines modifies the plasticity induction threshold of these spines, increasing the likelihood that synaptic activity at these sites will induce structural plasticity. Importantly, these data suggest that the autocrine BDNF-TrkB loop described above simultaneously exerts control over both homo- and heterosynaptic structural plasticity. </p><p>The final set of experiments reveals that the spreading of GTPase activity from stimulated spines helps to overcome the high activation thresholds of these proteins to facilitate nearby plasticity. Both Rac1 and RhoA, the activity of which spread into nearby spines, showed high activation thresholds, making weak stimuli incapable of activating them. Thus, signal spreading from a strongly stimulated spine can lower the plasticity threshold at nearby spines in part by supplementing the activation of high-threshold Rho GTPases at these sites. In contrast, the highly compartmentalized Rho GTPase Cdc42 showed a very low activation threshold, and thus did not require signal spreading to achieve high levels of activity to even a weak stimulus. As a result, synaptic crosstalk elicits cooperativity of nearby synaptic events by first priming a local region of the dendrite with several (but not all) of the factors required for structural plasticity, which then allows even weak inputs to achieve plasticity by means of localized Cdc42 activation. </p><p>Taken together, these data reveal a molecular pattern whereby BDNF-dependent structural plasticity can simultaneously maintain input-specificity while also relaying heterosynaptic signals along a local stretch of dendrite via coordination of differential spatial signaling profiles of the Rho GTPase proteins. The combination of this division of spatial signaling patterns and different activation thresholds reveals a unique heterosynaptic coincidence detection mechanism that allows for cooperative expression of structural plasticity when spines are close together, which in turn provides a putative mechanism for how neurons arrange structural modifications during learning.</p> / Dissertation

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.