Graduate

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Predicting well inflow performance relationship accurately is very important for production engineers. From these predictions, future plans for handling and improving well performance can be established. One method of predicting well inflow performance is to use artificial neural networks. Vogel's reference curve, which is produced from a series of simulation runs for a reservoir model proposed by Weller, is typically used to predict inflow performance relationship for solution-gas-drive reservoirs. In this study, I reproduced Vogel's work, but instead of producing one curve by conventional regression, I built three neural network models. Two models predict the IPR efficiently with higher overall accuracy than Vogel's reference curve.

DNA methylation-dependent gene silencing, catalyzed by DNA methyltransferases (DNMTs) and mediated by methyl binding domain proteins (MBDs) and histone deacetylases (HDACs), is essential for mammalian development, with the nervous system demonstrating particular sensitivity to perturbations. Little is known, however, about the role of DNA methylation in the stage-specific differentiation of neurons. In the olfactory epithelium (OE), where neurogenesis is continuous and the cells demonstrate a laminar organization with a developmental hierarchy, we identified sequential, transitional stages of differentiation likely mediated by different DNMT, MBD and HDAC family members. Biochemically, HDAC1 and HDAC2 associate with repressor complexes recruited by both MBD2 and MeCP2. HDAC1 and HDAC2, however, are divergently expressed in the OE, a pattern that is recapitulated in the brain. Rather than simultaneous inclusion in a complex, therefore, the individual association of HDAC1 or HDAC2 may provide specificity to a repressor complex in different cell types. Furthermore, distinct transitional stages of differentiation are perturbed in the absence of MBD2 or MeCP2. MeCP2 is expressed in the most apical immature olfactory receptor neurons (ORNs), and is up-regulated with neuronal maturation. In the MeCP2 null OE there is a transient delay in ORN maturation and an increase in neurons of an intermediate developmental stage. Two protein variants of MBD2 are expressed in the OE, with MBD2b expressed in cycling progenitor cells and MBD2a in the maturing ORNs. MBD2 null ORNs undergo increased apoptotic cell death. There is also a significant increase in proliferating progenitors in the MBD2 null OE, likely due, at least in part, to feedback from the dying ORNs, acting to up-regulate neurogenesis. Increased cell cycling in the MBD2 null is also observed post-lesion, however, in the absence of feedback back from the ORNs, a phenotype that is recapitulated by an acute inhibition of HDACs with valproic acid. Therefore, disruptions at both transitional stages of ORN differentiation are likely in the MBD2 null mouse. Together, these results provide the first evidence for a sequential recruitment of different MBD proteins and repressor complexes at distinct transitional stages of neuronal differentiation.

Uncertainty is inherent in neural processing due to noise in sensation and the sensory transmission processes, the ill-posed nature of many perceptual tasks, and temporal dynamics of the natural environment, to name a few causes. A wealth of evidence from physiological and behavioral experiments show that these various forms of uncertainty have major effects on perceptual learning and inference. In order to use sensory inputs efficiently to make decisions and guide behavior, neural systems must represent and manipulate information about uncertainty in their computations. In this thesis, we first consider how spiking neural populations might encode and decode information about continuous dynamic stimulus variables including the uncertainty about them. We explore the efficacy of a complex encoder that is paired with a simple decoder which allows computationally straightforward representation and manipulation of dynamically changing uncertainty. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner, by a simple, local learning rule. We also demonstrate that the coding scheme can be applied recursively to carry out meaningful uncertainty-sensitive computations such as dynamic cue combination. Next, we explore the computational principles that underlie non-linear response characteristics such as perceptual bias and uncertainty observed in audiovisual spatial illusions that involve multisensory interactions with conflicting cues. We examine in detail, the explanatory power of one particular causal model in characterizing the impact of conflicting inputs on perception and behavior. We also attempt to understand from a computational perspective, whether and how different task instructions might modulate the interaction of information from individual (visual and auditory) senses. Our analyses reveal some new properties of the sensory likelihoods and stimulus prior which were thought to be well described by Gaussian functions. Our results conclude that task-specific expectations can influence perception in ways that relate to a choice of inference strategy.

Uncertainty is inherent in neural processing due to noise in sensation and the sensory transmission processes, the ill-posed nature of many perceptual tasks, and temporal dynamics of the natural environment, to name a few causes. A wealth of evidence from physiological and behavioral experiments show that these various forms of uncertainty have major effects on perceptual learning and inference. In order to use sensory inputs efficiently to make decisions and guide behavior, neural systems must represent and manipulate information about uncertainty in their computations. In this thesis, we first consider how spiking neural populations might encode and decode information about continuous dynamic stimulus variables including the uncertainty about them. We explore the efficacy of a complex encoder that is paired with a simple decoder which allows computationally straightforward representation and manipulation of dynamically changing uncertainty. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner, by a simple, local learning rule. We also demonstrate that the coding scheme can be applied recursively to carry out meaningful uncertainty-sensitive computations such as dynamic cue combination. Next, we explore the computational principles that underlie non-linear response characteristics such as perceptual bias and uncertainty observed in audiovisual spatial illusions that involve multisensory interactions with conflicting cues. We examine in detail, the explanatory power of one particular causal model in characterizing the impact of conflicting inputs on perception and behavior. We also attempt to understand from a computational perspective, whether and how different task instructions might modulate the interaction of information from individual (visual and auditory) senses. Our analyses reveal some new properties of the sensory likelihoods and stimulus prior which were thought to be well described by Gaussian functions. Our results conclude that task-specific expectations can influence perception in ways that relate to a choice of inference strategy.

Several investigators have suggested that "upper" neural tube defects (NTD's)--anencephaly, encephalocele, and thoracic spina bifida--are etiologically different from "lower" NTD's (lumbo-sacral spina bifida). This hypothesis was primarily based on the observations that the two types have different sex ratios and recurrence rates and that the NTD cases within one sibship are concordant for NTD type. In order to test this, the above figures were calculated in a sample of NTD probands from Montreal and Newfoundland. The findings were not consistent with the hypothesis. However, a previously unreported finding was observed: the frequency of twinning was significantly higher in the near relatives of upper NTD probands than in those of lower NTD probands or of controls. This curious association between upper NTD's and twinning may be explained by a familial factor predisposing to a delay early in development. This delay could also explain any differences observed in upper and lower NTD groups.

A theoretical treatment has been combined with practical methods to obtain quantitative measurements from the transient responses of electrically coupled cells. Communication via electrical synapses is characterized as a low resistance pathway, whereas membrane response is represented by a resistance in parallel with a capacitance. / The validity and consequences of this model have been investigated using data from identified pairs of electrically coupled neurons in the freshwater snail, Helisoma trivolvis. An automated procedure has been developed to monitor the degree of coupling over extended periods of time and transform these measurements into equivalent electrical units. / The model has been used to: functionally assess the coupled system employing a single micropipette; provide a spatial profile of cell load; monitor induced changes in coupling; examine the role of coupling in controlling the rate of spread of excitation; and evaluate electrical changes which occur within cells during axonal injury and subsequent recovery.

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