Transient Receptor Potential ‘Melastatin’ 7 (TRPM7) is a ubiquitously expressed, non-selective divalent cation channel implicated in diverse cellular functions including actomyosin cytoskeletal remodeling, magnesium homeostasis, and anoxic neuronal death. The present study investigates the role of TRPM7 in modulating neuronal morphology and regulating neuronal cytoskeletal dynamics after anoxia. Overexpression of GFP-tagged TRPM7 in neuronal cultures caused a stunted morphology with fewer neurite branches than controls, suggesting that TRPM7 regulates the neuronal cytoskeleton during dendritic outgrowth. I have discovered that TRPM7 may regulate morphology via activation of cofilin-1 (an actin binding protein). I found that TRPM7-dependent cofilin activation during anoxia mediated neuronal death. Overall my work reveals a novel link between anoxia-induced TRPM7 activity and cofilin activation, which likely contributes to neurodegeneration after ischemia.

Difficulties and dangers in doing experiments on living systems and providing a testbed for theorists make the biologically detailed neural simulation an essential part of neurobiology. Due to the complexity of the neural systems and dynamic properties of the neurons simulation of biologically realistic models is very challenging area. Currently all general purpose simulator are software based. Limitation on the available processing power provides a huge gap between the maximum practical simulation size and human brain simulation as the most complex neural system. This thesis aimed at providing a hardware friendly parallel architecture in order to accelerate the simulation process.<p> This thesis presents a scalable hierarchical architecture for accelerating simulations of large-scale biological neural systems on field-programmable gate arrays (FPGAs). The architecture provides a high degree of flexibility to optimize the parallelization ratio based on available hardware resources and model specifications such as complexity of dendritic trees. The whole design is based on three types of customized processors and a switching module. An addressing scheme is developed which allows flexible integration of various combination of processors. The proposed addressing scheme, design modularity and data process localization allow the whole system to extend over multiple FPGA platforms to simulate a very large biological neural system.<p> In this research Hodgkin-Huxley model is adopted for cell excitability. Passive compartmental approach is used to model dendritic tree with any level of complexity. The whole architecture is verified in MATLAB and all processor modules and the switching unit implemented in Verilog HDL and Schematic Capture. A prototype simulator is integrated and synthesized for Xilinx V5-330t-1 as the target FPGA. While not dependent on particular IP (Intellectual Property) cores, the whole implementation is based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier cores. The synthesize results and performance analyses are provided.

The establishment of correct neural circuitry in the nervous system requires the interplay, integration, and coordination of a diverse set of cells and signals during development and in the adult. Two important events are the regulated initiation and growth of dendrites that receive and process synaptic information, and the establishment and maintenance of appropriate neural connectivity. The goals of this study are to identify the molecular mechanisms underlying dendrite growth and initiation, and to understand how neural connectivity is maintained in the adult nervous system. I first identified a novel intracellular signal transduction pathway involving two kinases important in regulating dendrite development. I showed that the ILK-GSK3beta pathway is required for dendrite growth and initiation in both peripheral and central nervous system neurons. I then asked how neural connectivity is maintained in the adult nervous system by examining the role of myelin in the intact nervous system. My results indicate that when myelin contacts aberrantly growing axons, it activates on those axons the p75 neurotrophin receptor (p75NTR), which in turn causes the local degeneration of those axons. I further identified the signal transduction pathway required for axon degeneration consisting of p75NTR and intracellular signaling proteins activated by this receptor, Rho-GDI, Rho, and caspase 6. This data establishes p75NTR as an important regulator of neural connectivity and identifies for the first time a degeneration-inducing signal transduction pathway activated by myelin. It also provides an explanation for why myelin inhibits regeneration of injured central nervous system axons. Taken together, I identified a new signaling pathway important for regulating dendrite initiation and growth, and a novel role for myelin in maintaining neural connectivity. Both of these findings contribute to our knowledge of how such connectivity is established during development and maintained in the adult nervous system.

Difficulties and dangers in doing experiments on living systems and providing a testbed for theorists make the biologically detailed neural simulation an essential part of neurobiology. Due to the complexity of the neural systems and dynamic properties of the neurons simulation of biologically realistic models is very challenging area. Currently all general purpose simulator are software based. Limitation on the available processing power provides a huge gap between the maximum practical simulation size and human brain simulation as the most complex neural system. This thesis aimed at providing a hardware friendly parallel architecture in order to accelerate the simulation process.<p> This thesis presents a scalable hierarchical architecture for accelerating simulations of large-scale biological neural systems on field-programmable gate arrays (FPGAs). The architecture provides a high degree of flexibility to optimize the parallelization ratio based on available hardware resources and model specifications such as complexity of dendritic trees. The whole design is based on three types of customized processors and a switching module. An addressing scheme is developed which allows flexible integration of various combination of processors. The proposed addressing scheme, design modularity and data process localization allow the whole system to extend over multiple FPGA platforms to simulate a very large biological neural system.<p> In this research Hodgkin-Huxley model is adopted for cell excitability. Passive compartmental approach is used to model dendritic tree with any level of complexity. The whole architecture is verified in MATLAB and all processor modules and the switching unit implemented in Verilog HDL and Schematic Capture. A prototype simulator is integrated and synthesized for Xilinx V5-330t-1 as the target FPGA. While not dependent on particular IP (Intellectual Property) cores, the whole implementation is based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier cores. The synthesize results and performance analyses are provided.

The establishment of correct neural circuitry in the nervous system requires the interplay, integration, and coordination of a diverse set of cells and signals during development and in the adult. Two important events are the regulated initiation and growth of dendrites that receive and process synaptic information, and the establishment and maintenance of appropriate neural connectivity. The goals of this study are to identify the molecular mechanisms underlying dendrite growth and initiation, and to understand how neural connectivity is maintained in the adult nervous system. I first identified a novel intracellular signal transduction pathway involving two kinases important in regulating dendrite development. I showed that the ILK-GSK3beta pathway is required for dendrite growth and initiation in both peripheral and central nervous system neurons. I then asked how neural connectivity is maintained in the adult nervous system by examining the role of myelin in the intact nervous system. My results indicate that when myelin contacts aberrantly growing axons, it activates on those axons the p75 neurotrophin receptor (p75NTR), which in turn causes the local degeneration of those axons. I further identified the signal transduction pathway required for axon degeneration consisting of p75NTR and intracellular signaling proteins activated by this receptor, Rho-GDI, Rho, and caspase 6. This data establishes p75NTR as an important regulator of neural connectivity and identifies for the first time a degeneration-inducing signal transduction pathway activated by myelin. It also provides an explanation for why myelin inhibits regeneration of injured central nervous system axons. Taken together, I identified a new signaling pathway important for regulating dendrite initiation and growth, and a novel role for myelin in maintaining neural connectivity. Both of these findings contribute to our knowledge of how such connectivity is established during development and maintained in the adult nervous system.

(運動・神経生理)

Drosophila is a simple and genetically tractable model system for studying neural circuits. This dissertation consists of two studies, with the broad goal of understanding sensory processing in neural circuits using Drosophila as a model system.

MECHANISMS OF DEPOLARIZATION INDUCED DENDRITIC GROWTH OF DROSOPHILA MOTOR NEURONS Cortnie Lauren Cherry The University of Arizona, 2006 Director: Richard B. Levine The study of the cellular mechanisms underlying dendritic growth contributes to our understanding of nervous system development, function and disease. Electrical activity is a fundamental property of neurons, and this property is utilized to influence the mechanisms involved in dendrite formation and maturation. Here we employ the Drosophila transgenic system to quantify dendritic growth of identified motor neurons using both in vitro and in vivo techniques. Two novel techniques are introduced: one a system to visualize and measure dendritic outgrowth in cultured neurons using reporter proteins, and the other using 3D reconstruction to measure the arborization of identified motor neurons in vivo. Both transgenic manipulation of K+ channel function and depolarizing concentrations of K+ in the culture medium result in an acceleration of dendritic outgrowth. Depolarization induced outgrowth is dependent on Plectreurys Toxin (PLTX)-sensitive voltage-gated calcium current and protein synthesis in cultured motor neurons. Depolarization leads to direct induction of fos, a protein that heterodimerizes with jun to make the functional transcription factor, AP-1. Fos, but not jun, is necessary for basal levels of dendritic growth, while both are necessary for depolarization induced outgrowth. Over-expression of AP-1 in control cells is sufficient to cause dendritic outgrowth. The transcription factor Adf-1 is also necessary for basal and depolarization induced growth, but unlike AP-1 is not sufficient to cause outgrowth when over-expressed. Another transcription factor CREB, on the other hand, is not necessary for basal levels of dendritic growth, but is necessary for depolarization induced dendritic growth. Over-expression of CREB, like Adf-1, is not sufficient to cause dendritic outgrowth. These findings present exciting new techniques for the study of the field of dendritic regulation and contribute to our understanding of the cellular mechanisms underlying dendritic growth.

Despite the importance of proper synaptogenesis in the CNS, the molecular mechanisms that regulate the formation and development of synapses remain poorly understood. Indeed, the mechanisms through which initial synaptic contacts are established and modified during synaptogenesis have not been fully determined and a precise understanding of these mechanisms may shed light on synaptic development, plasticity and many CNS developmental diseases. The development and formation of spiny synapses has been thought to occur via filopodia shortening followed by the recruitment of proper postsynaptic proteins, however the precise function of filopodia remains controversial. Thus the goal of this study was to investigate the dynamics of dendritic filopodia and determine their role in the development of synaptic contacts. We initially define and characterize short lipidated motifs that are sufficient to induce process outgrowth. Indeed, the palmitoylated protein motifs of GAP-43 and paralemmin are sufficient to induce filopodial extensions in heterologous cells and to increase the number of filopodia and dendritic branches in neurons. We showed that the morphological changes induced by these FIMs (filopodia inducing motifs) require on-going protein palmitoylation and are modulated by a specific GTPase, Cdc42, that regulates actin dynamics. We also show that their function is palmitoylation dependent and is dynamically regulated by reversible protein palmitoylation. Significantly, our work suggests a general role for those palmitoylated motifs in the development of structures important for synapse formation and maturation. We combined several approaches to monitor the formation and development of filopodia. We show that filopodia continuously explore the environment and probe for appropriate contacts with presynaptic partners. We find that shortly after establishing a contact with axons, filopodia induce the recruitment of presynaptic elements. Remarkably, we find that expression of acylated motifs or the constitutively active form of cdc-42 enhances filopodia number and motility, but reduces the recruitment of synaptophysin positive presynaptic elements and the probability of forming stable axo-dendritic contacts. We provide evidence for the rapid transformation of filopodia to spines within hours of imaging live neurons and reveal potential molecules that accelerate this process.

Magistriniame darbe tiriamos rašto ženklų atpažinimo problemos, nagrinėjami neuroniniai tinklai skirti rašto ženklams atpažinti. Apžvelgiamos problemos, kylančios sprendžiant rašto atpažinimo uždavinius, įvairūs problemų sprendimų būdai. Pasiūlytas dirbtinio neuroninio tinklo mokymo strategijos, pagrįstos klaidos skleidimo atgal algoritmu, patobulinimas. Patobulinimo esmė yra ta, kad mokymo aibė į tinklą paduodama ne visa iškarto, o dalimis. Kai neuroninis tinklas išmoksta atpažinti tą dalį, mokymo aibė papildoma naujais duomenimis, bet pradinių svorių vektoriai nesikeičia ir tinklas mokomas toliau. Šis algoritmo patobulinimas leidžia ženkliai sumažinti apmokymo laiką neprarandant tikslumo. Kai kuriais atvejais neuroninis tinklas, mokomas pagal įprastą klaidos skleidimo atgal algoritmą, nerodė jokių mokymo perspektyvų, po ~ 9 val. paklaida nesikeisdavo, neuroninis tinklas negalėdavo teisingai atpažinti nei vienos raidės. Panaudojus patobulintą strategiją, mus tenkinanti paklaida pasiekiama po ~ 3 val. 30 min. Taip pat darbe tiriama atpažinimo tikslumo priklausomybė nuo svorių pradinių reikšmių ir neuronų skaičiaus paslėptuose sluoksniuose. Nustatyti intervalai, kuriuose turi būti generuojamos svorių pradinės reikšmės, siekiant gauti tikslius atpažinimo rezultatus. Neuronų skaičius paslėptuose sluoksniuose turi būti daugiau nei penki. / The master thesis presents investigations of the problems of the optical character recognition. It also deals with the artificial neural networks that are designed for the optical character recognition. The work surveys the problems that emerge during the process of the optical character recognition. Various solutions are investigated. The improvement of a strategy for teaching the neural network that is based on the error back propagation is suggested. The essence of the improvement is that the training data set is divided into some parts and these parts are presented to the network one by one. When the neural network learns to recognize a part, the next part is presented to the network without any changes of the initial weight vectors and the network is trained further. This improvement allows us to reduce the training time significantly without losing the recognition accuracy. In some cases, the neural network that is trained according the ordinary error back propagation algorithm does not show any prospects. After ~ 9 hours, the error remains the same, the neural networks cannot recognize any letters. Using the improved strategy, the error satisfied is reached after ~3 hours 30 minutes. The dependence of the recognition accuracy on the values of the initial weight vectors and on the number of neurons in hidden layers is also investigated. The intervals in which the values of the initial weight vectors must be generated are identified, in order to get the correct results... [to full text]