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No description available.

<p>Trafficking of the glutamatergic AMPA receptors (AMPARs) has been implicated in synaptic plasticity regulation, including long-term potentiation, long-term depression, and synaptic scaling. Two proteins, GRIP for stabilization at the synapse and PICK for internalization, are involved in trafficking GluR2-containing AMPARs in and out of the synapse. In this thesis, I addressed the changes in the mechanisms of AMPAR trafficking by characterizing the developmental trajectories of GluR2, the phosphorylated form pGluR2, GRIP, and PICK and comparing expression in visual vs. frontal cortex. I found significant differences between cortical areas in the developmental trajectories of GluR2 and pGluR2. In visual cortex, expression levels exhibited smooth developmental increases. In frontal cortex, GluR2 and pGluR2 rose to an exuberant expression between P18 and P35. Developmental trajectories for GRIP and PICK showed smooth increases that were consistent across cortical areas. Furthermore, looking at the correlation between the surface components (GluR2 and GRIP) and internalized components (pGluR2 and PICK), I found that the development of AMPAR trafficking components is tightly regulated across the cortex.</p> <p>In this thesis, I also looked at AMPAR expression in adult cortex. Fluoxetine has previously been reported to induce a juvenile like state of plasticity in visual cortex and this plasticity was assessed through monocular deprivation. My results indicated that fluoxetine administration was not associated with significant changes in AMPAR expression levels. However, monocular deprivation induced significant upregulation in expression levels of all four proteins. These results imply the presence of AMPAR-mediated plasticity in the adult brain.</p> / Master of Science (MSc)

<p> Alterations in neural tissues associated with environmental variables have been studied for many years. Anatomical changes in the neocortex of rats in response to exposure to complex environments were observed and replicated in a number of studies both within and across species. These changes are not dependent on the age of the animal or on the duration of exposure, and have been demonstrated in structures outside of the cortex. Due to the undisputed involvement of both the neocortex and the hippocampus in learning and memory, researchers applied a widely used model system of a synaptic mechanism for learning, long-term potentiation (LTP), to the environmental enrichment paradigm and demonstrated significant enhancements in hippocampal field potentials in enriched rats. The present study examines whether the neocortex also showed evidence of plasticity in synaptic transmission. No effects for environmental enrichment were observed on the maximum amplitude of neocortical field responses evoked from the corpus callosum. To assess the plasticity of the chronic preparation used in the study, the animals were exposed to trains of pulses previously shown to induce electrical LTP in the cortex, but revealed only a slight, although significant, depression of the evoked response amplitude. An alteration in the stimulation parameters did not result in an enhanced response. Cortical depth measures suggested that the enriched environment was indeed sufficient to produce plastic changes in anatomy, if not in the efficacy of synaptic transmission. The importance of these findings in the neocortex leads us to question the validity of the LTP model of learning and memory.</p> / Thesis / Master of Science (MSc)

Synaptic vesicle recycling is the reuse of synaptic membrane material and proteins after vesicles have been exocytosed at the pre-synaptic terminal of a neuronal synapse. The discovery of the mechanisms by which recycling operates is a subject of active research. Within small mammalian central nervous system nerve terminals, two studied mechanisms of recovery are clathrin-mediated endocytosis and activity-dependent bulk endocytosis. Research into the comparative kinetics and mechanisms underlying these endocytosis mechanisms commonly involves time-series fluorescence microscopy of in vitro cultures. Synaptic proteins are tagged with fluorescent markers, or the synaptic vesicles are labelled with fluorescent dye. The change in fluorescence levels of individual synapses over time in response to stimuli is used to understand synaptic activity. The image analysis of these time-series images frequently requires substantial manual effort to extract the changing synaptic fluorescence intensity levels over time. This work focusses on two closely interlinked areas, the development of improved automated image analysis tools to facilitate the analysis of microscopy image data, and computational simulations to leverage the data obtained from these experiments to gain mechanistic insight into the underlying processes involved in synaptic vesicle recycling. The imaged properties of synapses within the time-series images are characterised, in terms of synapse movement during the course of an experiment. This characterisation highlights the properties which risk adding error to the extracted fluorescence intensity data, as analysis generally requires segmentation of regions of interest with fixed size and location. Where possible, protocols to optimise the manual selection of synapses in the image are suggested. The manual selection of synapses within time-series images is a common but time consuming and difficult task. It requires considerable skill on the part of the researcher to select synapses from noisy images without introducing error or bias. Automated tools for either general image segmentation or for segmentation of synapse-like puncta do exist, but have mixed results when applied to time-series experiments. This work introduces the use of knowledge of the experiment protocol into the segmentation process. The selection of synapses as they respond to known stimuli is compared against other current segmentation methods, and tools to perform this segmentation are provided. This use of synapse activity improves the quality of the segmented set of synapses over existing segmentation tools. Finally, this work builds a number of computational models, to allow published individual data points to be aggregated into a coherent view of overall synaptic vesicle recycling. The first is FM-Sim, a stochastic hybrid model of overall synapse recycling as is expected to occur during the course of an experiment. This closed system model handles the processes of exocytosis and endocytosis. It uses Bayesian inference to fit model parameters to experimental data. In particular, it uses the experimental protocol to separate the mechanisms and rates that may contribute to the observed experimental data. The second is a mathematical model of one aspect of synaptic vesicle recycling of particular interest - homoeostasis of plasma membrane integrity on the presynaptic terminal. This model provides bounds on efficiency of the studied endocytosis mechanisms at recovery of plasma membrane area during and after neuronal stimulus. Both the image analysis and the computational simulations demonstrated in this work provide useful tools and insights into current research of synaptic vesicle recycling and the role of activity-dependent bulk endocytosis. In particular, the utility of adding time-dependent experimental protocol knowledge to both the image analysis tools and the computational simulations is shown.

Thesis (Ph.D. in Pharmacology) -- University of Colorado Denver, 2007. / Typescript. Includes bibliographical references (leaves 132-151). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;

Synapses vary widely in the probability of transmitter release. For instance, in response to an action potential the phasic synapses of the crayfish have a 100-1000-fold higher release probability than tonic synapses. The difference in release probability is attributed to differences in the exocytotic machinery such as the degree of “zippering” of the trans-SNARE (Soluble N-ethylmaleimide-sensitive factor Attachment protein REceptor) complex. I used physiological and molecular approaches to determine if the zippered state of SNAREs associated with synaptic vesicles and the interaction between the SNARE complex and Complexin influence the probability of release at the synapse. I used three Botulinum neurotoxins which bind and cleave at different sites on VAMP to determine whether these sites were occluded by SNARE interaction (zippering) or open to proteolytic attack. Under low stimulation conditions, the light-chain fragment of botulinum B (BoNT/B-LC) but not BoNT/D-LC or tetanus neurotoxin (TeNT-LC) cleaved VAMP and inhibited evoked release at both phasic and tonic synapses. In addition, a peptide based on the C-terminal half of crayfish VAMP’s SNARE motif (Vc peptide) designed to interfere with SNARE complex zippering at the C-terminal end inhibited release at both synapses. The susceptibility of VAMP to only BoNT/B-LC and interference by the Vc peptide indicated that SNARE complexes at both phasic and tonic synapses were partially zippered only at the N-terminal end with the C-terminal end exposed under resting conditions. I used a peptide containing part of the crayfish Complexin central α-helix domain to interfere with the interaction between Complexin and the SNARE complex. The peptide enhanced phasic evoked release and inhibited tonic evoked release under low stimulation but attenuated release at both synapses under intense stimulation. Therefore, Complexin appeared to exhibit a dual function under low synaptic activity but only promoted release under high synaptic activity. The results showed that the zippered state of the SNARE complex does not determine initial release probability as a similar zippered SNARE complex structure under resting conditions is common to both phasic and tonic synapses. However, Complexin may have a role in influencing the initial release probability of a synapse. Therefore, the interaction between the SNARE complex and Complexin is important for release but other factors contribute more significantly to synaptic strength.

Synapses vary widely in the probability of transmitter release. For instance, in response to an action potential the phasic synapses of the crayfish have a 100-1000-fold higher release probability than tonic synapses. The difference in release probability is attributed to differences in the exocytotic machinery such as the degree of “zippering” of the trans-SNARE (Soluble N-ethylmaleimide-sensitive factor Attachment protein REceptor) complex. I used physiological and molecular approaches to determine if the zippered state of SNAREs associated with synaptic vesicles and the interaction between the SNARE complex and Complexin influence the probability of release at the synapse. I used three Botulinum neurotoxins which bind and cleave at different sites on VAMP to determine whether these sites were occluded by SNARE interaction (zippering) or open to proteolytic attack. Under low stimulation conditions, the light-chain fragment of botulinum B (BoNT/B-LC) but not BoNT/D-LC or tetanus neurotoxin (TeNT-LC) cleaved VAMP and inhibited evoked release at both phasic and tonic synapses. In addition, a peptide based on the C-terminal half of crayfish VAMP’s SNARE motif (Vc peptide) designed to interfere with SNARE complex zippering at the C-terminal end inhibited release at both synapses. The susceptibility of VAMP to only BoNT/B-LC and interference by the Vc peptide indicated that SNARE complexes at both phasic and tonic synapses were partially zippered only at the N-terminal end with the C-terminal end exposed under resting conditions. I used a peptide containing part of the crayfish Complexin central α-helix domain to interfere with the interaction between Complexin and the SNARE complex. The peptide enhanced phasic evoked release and inhibited tonic evoked release under low stimulation but attenuated release at both synapses under intense stimulation. Therefore, Complexin appeared to exhibit a dual function under low synaptic activity but only promoted release under high synaptic activity. The results showed that the zippered state of the SNARE complex does not determine initial release probability as a similar zippered SNARE complex structure under resting conditions is common to both phasic and tonic synapses. However, Complexin may have a role in influencing the initial release probability of a synapse. Therefore, the interaction between the SNARE complex and Complexin is important for release but other factors contribute more significantly to synaptic strength.

No description available.

Biological neural systems (BNS) in general and the central nervous system (CNS) specifically exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms of information processing within the BNS and their computational capabilities is a core objective of modern computer science, computational sciences and neuroscience. Among the main reasons of this tendency to understand the brain is to help in improving the quality of life of people suffer from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces (BCI), neural prostheses and other similar approaches are potential solutions either to help these patients through therapy or to push the progress in rehabilitation. There is however a significant lack of knowledge regarding the basic information processing within the CNS. Without a better understanding of the fundamental operations or sequences leading to cognitive abilities, applications like BCI or neural prostheses will keep struggling to find a proper and systematic way to help patients in this regard. In order to have more insights into these basic information processing methods, this thesis presents an approach that makes a formal distinction between the essence of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with expert systems. This approach investigates the underlying mechanisms allowing the CNS to be capable of performing a massive amount of computational tasks with a sustainable efficiency and flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent. The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities. These capabilities can be imported into spiking neural networks (SNN) by emulating the dynamic neural system. Emulating the dynamic system requires simulating both the inner workings of the system and the framework of performing the information processing tasks. Thus, this work comprises two main parts. The first part is concerned with introducing a proper and a novel dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system. This model represents a balanced integration between the needed biophysical details and being computationally inexpensive. Being a biophysical model is important to allow for the abilities of the target dynamic system to be inherited, and being simple is needed to allow for further implementation in large scale simulations and for hardware implementation in the future. Besides, the energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks seeking for stable states of activities. The second part of the thesis is consequently concerned with importing the processing framework of the dynamic system into the environment of SNN. This part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The concepts of computing with states are extended to investigate a computational model that is based on the finite-state machines and reservoir computing. Biological plausible validations of the introduced model and frameworks are performed. Results and discussions of these validations indicate that this study presents a significant advance on the way of empowering the knowledge about the mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on how to adopt the biological computational capabilities in computation science in general and in biologically-inspired spiking neural networks in specific. Large scale simulations and the development of neuromorphic hardware are work-in-progress and future work. Among the applications of the introduced work are neural prostheses and bionic automation systems.