Spinal Muscular Atrophy (SMA) is a neuronal degenerative disease caused by the mutation or loss of the Survival Motor Neuron (SMN) gene. The cause for the specific motor neuron susceptibility in SMA has not been identified. The high axonal transport/localization demand on motor neurons may be one potentially disrupted function, more specific to these cells. We therefore used a large-scale immunoprecipitation (IP) experiment, to identify potential interactors of SMN involved in neuronal transport and localization of mRNA targets. We identified KH-type splicing regulatory protein (KSRP), a multifunctional RNA-binding protein that has been implicated in transcriptional regulation, neuro-specific alternative splicing, and mRNA decay. KSRP is closely related to chick zipcode-binding protein 2 and rat MARTA1, proteins involved in neuronal transport/localization of beta-actin and microtubule-associated protein 2 mRNAs, respectively. We demonstrated that KSRP is arginine methylated, a novel SMN interactor (specifically with the SMN Tudor domain; and not with SMA causing mutants). We also found this protein to be misregulated in the absence of SMN, resulting in increased mRNA stability of KSRP mRNA target, p21cip/waf1. A role for SMN as an axonal chaperone of methylated RBPs could thus be key in SMA pathophysiology.

Induced pluripotent stem (iPS) cells generated from patients hold great promise for studying diseases that affect the central nervous system, as differentiation into the neuronal lineage creates a limitless supply of affected cells for disease study. Rett syndrome (RTT) is a neurodevelopmental autism spectrum disorder primarily caused by mutations in the methyl-CpG-binding protein 2 (MECP2) gene. Due to the inaccessibility of patient neurons, most of what is known about underlying phenotypes has been described using mouse models. iPS cells provide a potential solution, but reprogramming of patient cells is hampered by low efficiency, and early methods of identifying iPS cells involve transgenic techniques that are not translatable to human patient samples. The first part of this thesis describes the generation and characterization of a pluripotency reporter to address this issue. The EOS lentiviral reporter allows real-time observation of pluripotency changes during reprogramming, and is a useful tool for more efficient isolation of reprogrammed cell lines. Further, the EOS selection system can be used in a disease context to reproducibly mark and maintain disease-specific iPS cell lines for future use in disease modelling. Though iPS cells have been used to study RTT in vitro, extensive assessments of neuron function and electrophysiology have not yet been performed. In the second part of this thesis, iPS cell lines generated from a RTT mouse model were tested for their ability to model disease in vitro. Directed differentiation of multiple Mecp2-deficient and wild-type iPS cell lines to glutamatergic neurons revealed neurons that lack Mecp2 have a smaller soma size, diminished sodium currents, and are less excitable, firing fewer, prolonged action potentials that are smaller in magnitude. This deficiency in intrinsic excitability was accompanied by a dysfunction at excitatory glutamatergic synapses, which together recapitulate changes previously observed in the Mecp2-deficient mouse brain. Having accumulated counts and recordings from hundreds of neurons with consistent responses among lines, the iPS cell system is a representative model of the neuronal and synaptic defects in RTT. These results illustrate the requirement of MeCP2 in normal neuronal function, and suggest altered neuronal homeostasis or aberrant network circuitry may underlie RTT pathogenesis.

The teneurin C-terminal associated peptides (TCAP) are a family of four predicted peptides that are expressed in all metazoans where the teneurins have been studied to date. Of the four peptides, TCAP-1 has been studied most extensively. In vitro, TCAP-1 increases neuronal proliferation and neurite outgrowth. In vivo, the peptide reduces CRF-induced behavioural responses in rats. Despite the large body of evidence indicating a strong biological role for TCAP-1, little is known about the chemistry and solubility of the peptide, or the signaling pathway(s) mediating these effects. The aim of this research was to appropriately solubilize the peptide and to develop detection assays for its study in greater detail. I have now established an appropriate formulation of TCAP-1 and developed an immunoassay to assess its concentrations in tissues and in circulation. Also, by examining a number of transcriptional response elements, I have found two assays for probing the signal transduction mechanisms of this peptide.

The teneurin C-terminal associated peptides (TCAP) are a family of four predicted peptides that are expressed in all metazoans where the teneurins have been studied to date. Of the four peptides, TCAP-1 has been studied most extensively. In vitro, TCAP-1 increases neuronal proliferation and neurite outgrowth. In vivo, the peptide reduces CRF-induced behavioural responses in rats. Despite the large body of evidence indicating a strong biological role for TCAP-1, little is known about the chemistry and solubility of the peptide, or the signaling pathway(s) mediating these effects. The aim of this research was to appropriately solubilize the peptide and to develop detection assays for its study in greater detail. I have now established an appropriate formulation of TCAP-1 and developed an immunoassay to assess its concentrations in tissues and in circulation. Also, by examining a number of transcriptional response elements, I have found two assays for probing the signal transduction mechanisms of this peptide.

journal's webpage is available at http://www.karger.com/bbe .

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

Traumatic brain injury (TBI) results from a physical insult to the head and often results in temporary or permanent brain dysfunction. However, the cellular pathology remains poorly understood and there are currently no clinically effective treatments. The overall goal of this work was to develop and characterize a novel three-dimensional (3-D) in vitro paradigm of neural trauma integrating a robust 3-D neural co-culture system and a well-defined biomechanical input representative of clinical TBI. Specifically, a novel 3-D neuronal-astrocytic co-culture system was characterized, establishing parameters resulting in the growth and vitality of mature 3-D networks, potentially providing enhanced physiological relevance and providing an experimental platform for the mechanistic study of neurobiological phenomena. Furthermore, an electromechanical device was developed that is capable of subjecting 3-D cell-containing matrices to a defined mechanical insult, with a predicted strain manifestation at the cellular level. Following independent development and validation, these novel 3-D neural cell and mechanical trauma paradigms were used in combination to develop a mechanically-induced model of neural degeneration and reactive astrogliosis. This in vitro surrogate model of neural degeneration and reactive astrogliosis was then exploited to assess factors influencing neural stem cell (NSC) survival and integration upon delivery to this environment, revealing that specific factors in an injured environment were detrimental to NSC survival. This work has developed enabling technologies for the in vitro study of neurobiological phenomena and responses to injury, and may aid in elucidating the complex biochemical cascades that occur after a traumatic insult. Furthermore, the novel paradigm developed here may provide a powerful experimental framework for improving treatment strategies following neural trauma, and therefore serve as a valid pre-animal test-bed.

The dynamics of biological neural networks are of great interest to neuroscientists and are frequently studied using conductance-based compartmental neuron models. For speed and ease of use, neuron models are often reduced in morphological complexity. This reduction may affect input processing and prevent the accurate reproduction of neural dynamics. However, such effects are not yet well understood. Therefore, for my first aim I analyzed the processing capabilities of 'branched' or 'unbranched' reduced models by collapsing the dendritic tree of a morphologically realistic 'full' globus pallidus neuron model while maintaining all other model parameters. Branched models maintained the original detailed branching structure of the full model while the unbranched models did not. I found that full model responses to somatic inputs were generally preserved by both types of reduced model but that branched reduced models were better able to maintain responses to dendritic inputs. However, inputs that caused dendritic sodium spikes, for instance, could not be accurately reproduced by any reduced model. Based on my analyses, I provide recommendations on how to construct reduced models and indicate suitable applications for different levels of reduction. In particular, I recommend that unbranched reduced models be used for fast searches of parameter space given somatic input output data. The intrinsic electrical properties of neurons depend on the modifiable behavior of their ion channels. Obtaining a quality match between recorded voltage traces and the output of a conductance based compartmental neuron model depends on accurate estimates of the kinetic parameters of the channels in the biological neuron. Indeed, mismatches in channel kinetics may be detectable as failures to match somatic neural recordings when tuning model conductance densities. In my first aim, I showed that this is a task for which unbranched reduced models are ideally suited. Therefore, for my second aim I optimized unbranched reduced model parameters to match three experimentally characterized globus pallidus neurons by performing two stages of automated searches. In the first stage, I set conductance densities free and found that even the best matches to experimental data exhibited unavoidable problems. I hypothesized that these mismatches were due to limitations in channel model kinetics. To test this hypothesis, I performed a second stage of searches with free channel kinetics and observed decreases in the mismatches from the first stage. Additionally, some kinetic parameters consistently shifted to new values in multiple cells, suggesting the possibility for tailored improvements to channel models. Given my results and the potential for cell specific modulation of channel kinetics, I recommend that experimental kinetic data be considered as a starting point rather than as a gold standard for the development of neuron models.

Disputats. Rheinische Friedrick-Wilhelms-Universität, 2001.

Ce mémoire consiste en l’étude du comportement dynamique de deux oscillateurs FitzHugh-Nagumo identiques couplés. Les paramètres considérés sont l’intensité du courant injecté et la force du couplage. Juqu’à cinq solutions stationnaires, dont on analyse la stabilité asymptotique, peuvent co-exister selon les valeurs de ces paramètres. Une analyse de bifurcation, effectuée grâce à des méthodes tant analytiques que numériques, a permis de détecter différents types de bifurcations (point de selle, Hopf, doublement de période, hétéroclinique) émergeant surtout de la variation du paramètre de couplage. Une attention particulière est portée aux conséquences de la symétrie présente dans le système. / We study the dynamical behaviour of a pair of identical, coupled FitzHugh-Nagumo oscillators. We determine the parameter values leading to the existence of up to five equilibrium solutions, and analyze the asymptotic stability of each one. A combination of analytical and numerical techniques is used to analyze the numerous bifurcations (saddle-node, Hopf, period-doubling, heteroclinic) occurring as parameters, most notably the coupling strength, are varied, attention being paid to the rôle played by symmetries in the system.