Non-equilibrium systems have no set method of analysis, and a wide array of dynamics can be present in such systems. In this work we present three very different non-equilibrium models, inspired by biological systems and phenomena, that we analyze through computational means to showcase both the range of dynamics encompassed by these systems, as well as various techniques used to analyze them. The first system we model is a surface plasmon resonance (SPR) cell, a device used to determine the binding rates between various species of chemicals. We simulate the SPR cell and compare these computational results with a mean-field approximation, and find that such a simplification fails for a wide range of reaction rates that have been observed between different species of chemicals. Specifically, the mean-field approximation places limits on the possible resolution of the measured rates, and such an analysis fails to capture very fast dynamics between chemicals. The second system we analyzed is an avalanching neural network that models cascading neural activity seen in monkeys, rats, and humans. We used a model devised by Lombardi, Herrmann, de Arcangelis et al. to simulate this system and characterized its behavior as the fraction of inhibitory neurons was changed. At low fractions of inhibitory neurons we observed epileptic-like behavior in the system, as well as extended tails in the avalanche strength and duration distributions, which dominate the system in this regime. We also observed how the connectivity of these networks evolved under the effects of different inhibitory fractions, and found the high fractions of inhibitory neurons cause networks to evolve more sparsely, while networks with low fractions maintain their initial connectivity. We demonstrated two strategies to control the extreme avalanches present at low inhibitory fractions through either the random or targeted disabling of neurons. The final system we present is a sparsely encoding convolutional neural network, a computational system inspired by the human visual cortex that has been engineered to reconstruct images inputted into the network using a series of "patterns" learned from previous images as basis elements. The network attempts to do so "sparsely," so that the fewest number of neurons are used. Such systems are often used for denoising tasks, where noisy or fragmented images are reconstructed. We observed a minimum in this denoising error as the fraction of active neurons was varied, and observed the depth and location of this minimum to obey finite-size scaling laws that suggest the system is undergoing a second-order phase transition. We can use these finite-size scaling relations to further optimize this system by tuning it to the critical point for any given system size. / Doctor of Philosophy / Non-equilibrium systems have no set method of analysis, and a wide array of dynamics can be present in such systems. In this work we present three very different non-equilibrium models, inspired by biological systems and phenomena, that we analyze through computational means to showcase both the range of dynamics encompassed by these systems, as well as various techniques used to analyze them. The first system we model is a surface plasmon resonance (SPR) cell, a device used to determine the binding rates between various species of chemicals. We simulate the SPR cell and compare these computational results with a mean-field approximation, and find that such a simplification fails for a wide range of reaction rates that have been observed between different species of chemicals. Specifically, the mean-field approximation places limits on the possible resolution of the measured rates, and such an analysis fails to capture very fast dynamics between chemicals. The second system we analyzed is an avalanching neural network that models cascading neural activity seen in monkeys, rats, and humans. We used a model devised by Lombardi, Herrmann, de Arcangelis et al. to simulate this system and characterized its behavior as the fraction of inhibitory neurons was changed. At low fractions of inhibitory neurons we observed epileptic-like behavior in the system, as well as extended tails in the avalanche strength and duration distributions, which dominate the system in this regime. We also observed how the connectivity of these networks evolved under the effects of different inhibitory fractions, and found the high fractions of inhibitory neurons cause networks to evolve more sparsely, while networks with low fractions maintain their initial connectivity. We demonstrated two strategies to control the extreme avalanches present at low inhibitory fractions through either the random or targeted disabling of neurons. The final system we present is a sparsely encoding convolutional neural network, a computational system inspired by the human visual cortex that has been engineered to reconstruct images inputted into the network using a series of “patterns” learned from previous images as basis elements. The network attempts to do so “sparsely,” so that the fewest number of neurons are used. Such systems are often used for denoising tasks, where noisy or fragmented images are reconstructed. We observed a minimum in this denoising error as the fraction of active neurons was varied, and observed the depth and location of this minimum to obey finite-size scaling laws that suggest the system is undergoing a second-order phase transition. We can use these finite-size scaling relations to further optimize this system by tuning it to the critical point for any given system size.

The Winner-Take-All (WTA) is an essential neural network operation for locating the most active neuron. Such a procedure has been extensively used in larger application areas. The Winner-Take-All circuit selects the maximum of the inputs inhibiting all other nodes. The efficiency of the analog circuits may well be considerably higher than the digital circuits. Also, analog circuits’ design footprint and processing time can be significantly small. A current-based Winner-Take-All circuit for analog neural networks is presented in this research. A compare and pass (CAP) mechanism has been used, where each input pair is compared, and the winner is selected and passed to another level. The inputs are compared by a sense amplifier which generates high and low voltage signals at the output node. The voltage signal of the sense amplifier is used to select the winner and passed to another level using logic gates. Also, each winner follows a sequence of digital bits to be selected. The findings of the SPICE simulation are also presented. The simulation results on the MNIST, Fashion-MNIST, and CIFAR10 datasets for the memristive deep neural network model show the significantly accurate result of the winner class with an average difference of input and selected winner output current of 0.00795uA, 0.01076uA and 0.02364uA respectively. The experimental result with transient noise analysis is also presented.

Alzheimer's disease (AD) leads to neurodegeneration resulting in cognitive and physical impairments. AD is denoted by accumulation of intracellular neurofibrillary tangles, known as tau, and extracellular plaques of the amyloid beta protein (Aβ). Aβ results from the proteolytic cleavage of the amyloid precursor protein (APP) by β- and gamma-secretases in the amyloidogenic pathway. Although, Aβ has been widely studied for neurodegeneration, the role of APP in both, the healthy and diseased conditions, has not yet been entirely understood. The function that APP has in neural stem cell (NSC) proliferation, differentiation, and migration during adult neurogenesis has been previously studied. Additionally, APP has be shown to be overexpressed after neural damage resulted from conditions, such as AD and traumatic brain injury (TBI). In this study, the role of APP in in vitro damaged neural tissue cells was further investigated by evaluating neural progenitor cell proliferation, migration, and differentiation after a scratch assay. For these purposes, induced pluripotent stem (iPS) cells from AD patients were differentiated into neural progenitor cells to model the disease conditions and later treated with Phenserine to reduce their levels of APP expression. The results suggested that APP may enhance neural progenitor cell proliferation and glial differentiation while inhibiting neural progenitor cell migration and neuronal cell specialization after neural tissue damage.

No description available.

Adult hippocampal neurogenesis, or the process of creating new neurons in the dentate gyrus (DG) of the hippocampus, underlies learning and memory capacity. This cognitive ability is essential for humans to operate in their everyday lives, but cognitive disruption can occur in response to traumatic insult such as brain injury. Previous findings in rodent models have characterized the effect of moderate traumatic brain injury (TBI) on neurogenesis and found learning and memory shortfalls correlated with limited neurogenic capacity. While there are no substantial changes after one mild TBI, research has yet to determine if neurogenesis contributes to the worsened cognitive outcomes of repetitive mild TBI. Here, we examined the effect of neurogenesis on cognitive decline following repetitive mild TBI by utilizing AraC to limit the neurogenic capacity of the DG. Utilizing a BrdU fate-labeling strategy, we found a significant increase in the number of immature neurons that correlate learning and memory impairment. These changes were attenuated in AraC-treated animals. We further identified endothelial cell (EC)-specific EphA4 receptor as a key mediator of aberrant neurogenesis. Taken together, we conclude that increased aberrant neurogenesis contributes to learning and memory deficits after repetitive mild TBI. / Doctor of Philosophy / In the United States, millions of people experience mild traumatic brain injuries, or concussions, every year. Patients often have a lower ability to learn and recall new information, and those who go on to receive more concussions are at an increased risk of developing long-term memory-associated disorders such as dementia and chronic traumatic encephalopathy. Despite the high number of athletes and military personnel at risk for these disorders, the underlying cause of long-term learning and memory shortfalls associated with multiple concussions remains ill defined. In the brain, the hippocampus play an important role in learning and memory and is one of only two regions in the brain where new neurons are created from neural stem cells through the process of neurogenesis. Our study seeks to address the role of neurogenesis in learning and memory deficits in mice. These findings provide the foundation for future, long-term mechanistic experiments that uncover the aberrant or uncontrolled processes that derail neurogenesis after multiple concussions. In short, we found an increase in the number of newborn immature neurons that we classify as aberrant neurogenesis. Suppressing this process rescued the learning and memory problems in a rodent model of repeated concussion. These findings improve our understanding of the processes that contribute to the pathophysiology of TBI.

1. Lymnaea was the subject of an established behavioural conditioning paradigm where pairings of a neutral lip tactile stimulus (CS) and a sucrose food stimulus (US) results in a conditioned feeding response to the CS alone. The current objective was to dissect trained animals and examine electrophysiological changes in the feeding circuitry which may underlie this learning. 2. Naive subjects were used to confirm that US and CS responses in vivo persisted in vitro since this is a pre-requisite for survival of a learned memory trace. This required the development of a novel semi-intact preparation facilitating CS presentation and simultaneous access to the CNS. 3. The nature and function of the CS response was investigated using naive animals. Intracellular recordings revealed that the tactile CS evokes specific, consistent synaptic responses in identified feeding neurons. Extracellular recording techniques and anatomical investigations showed that these responses occurred through a direct pathway linking the lips to the feeding circuitry. A buccal neuron was characterized which showed lip tactile responses and supplied synaptic inputs to feeding neurons indicating that it was a second-order mechanosensory neuron involved in the CS pathway. 4. Animals trained using the behavioural conditioning paradigm were tested for conditioned responses and subsequently dissected~ Intracellular recording from specific identified feeding motoneurons revealed that CS presentation resulted in significant activation of the feeding network compared to control subjects. This activation was combined both with an increase in the amplitude of a specific synaptic input and an elevation in the extracellular spike activity recorded from a feeding-related connective. A neuronal mechanism to account for these findings is presented. 5. The role of motoneurons in the feeding circuit was reassessed. It is demonstrated, contrary to the current model, that muscular motoneurons have an important contribution during feeding rhythms through previously unreported electrotonic CPG connections.

No description available.

Little research has been carried out in order to understand the mechanisms underlying the perception of polyphonic music. Perception of polyphonic music involves thematic recognition, that is, recognition of instances of theme through polyphonic voices, whether they appear unaccompanied, transposed, altered or not. There are many questions still open to debate concerning thematic recognition in the polyphonic domain. One of them, in particular, is the question of whether or not cognitive mechanisms of segmentation and thematic reinforcement facilitate thematic recognition in polyphonic music. This dissertation proposes a connectionist model to investigate the role of segmentation and thematic reinforcement in thematic recognition in polyphonic music. The model comprises two stages. The first stage consists of a supervised artificial neural model to segment musical pieces in accordance with three cases of rhythmic segmentation. The supervised model is trained and tested on sets of contrived patterns, and successfully applied to six musical pieces from J. S. Bach. The second stage consists of an original unsupervised artificial neural model to perform thematic recognition. The unsupervised model is trained and assessed on a four-part fugue from J. S. Bach. The research carried out in this dissertation contributes into two distinct fields. Firstly, it contributes to the field of artificial neural networks. The original unsupervised model encodes and manipulates context information effectively, and that enables it to perform sequence classification and discrimination efficiently. It has application in cognitive domains which demand classifying either a set of sequences of vectors in time or sub-sequences within a unique and large sequence of vectors in time. Secondly, the research contributes to the field of music perception. The results obtained by the connectionist model suggest, along with other important conclusions, that thematic recognition in polyphony is not facilitated by segmentation, but otherwise, facilitated by thematic reinforcement.

No description available.

published_or_final_version / Community Medicine / Master / Master of Public Health