Spiking neural networks (SNNs) are a more biologically realistic model of the brain than traditional analog neural networks and therefore should be better for modelling certain functions of the human brain. This thesis uses the concept of deriving an SNN from an accepted non-spiking neural network via analysis and modifications of the transmission function. We investigate this process to determine if and how the modifications can be made to minimize loss of information during the transition from non-spiking to spiking while retaining positive features and functionality of the non-spiking network. By comparing combinations of spiking neuron models and networks against each other, we determined that replacing the transmission function with a neural model that is similar to it allows for the easiest method to create a spiking neural network that works comparatively well. This similarity between transmission function and neuron model allows for easier parameter selection which is a key component in getting a functioning SNN. The parameters all play different roles, but for the most part, parameters that speed up spiking, such as large resistance values or small rheobases generally help the accuracy of the network. But the network is still incomplete for a spiking neural network since this conversion is often only performed after learning has been completed in analog form. The neuron model and subsequent network developed here are the initial steps in creating a bidirectional SNN that handles hetero-associative and auto-associative recall and can be switched easily between spiking and non-spiking with minimal to no loss of data. By tying everything to the transmission function, the non-spiking learning rule, which in our case uses the transmission function, and the neural model of the SNN, we are able to create a functioning SNN. Without this similarity, we find that creating SNN are much more complicated and require much more work in parameter optimization to achieve a functioning SNN.

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Habitually poor posture can lead to repetitive strain injuries that lower an individual's quality of life and productivity. Slouching over computer screens and smart phones, asymmetric weight distribution due to uneven leg loading, and improper loading posture are some of the common examples that lead to postural problems and health ramifications. To help cultivate good postural habits, researchers have proposed slouching, balance, and improper loading posture detection systems that alert users through traditional visual, auditory or vibro-tactile feedbacks when posture requires attention. However, such notifications are disruptive and can be easily ignored. We address these issues with a new physiological feedback system that uses sensors to detect these poor postures, and electrical muscle stimulation to automatically correct the poor posture. We compare our automatic approach against other alternative feedback systems and through different unique contexts. We find that our approach outperformed alternative traditional feedback systems by being faster and more accurate while delivering an equally comfortable user experience.

This dissertation presents two lines of research that are superficially at opposite ends of the computational neuroscience spectrum. While models of adaptive motion detection in fruit flies and simulations inspired by monkeys that learn to control brain machine interfaces might seem like they have little in common, these projects both attempt to address the broad question of how real neural circuits flexibly compute. Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. The first part of this dissertation analyzes the filtering properties of four neural inputs to the OFF motion-detecting T5 cell in Drosophila. These four neurons, Tm1, Tm2, Tm4 and Tm9, exhibit state- and stimulus-dependent changes in the shape of their temporal responses, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, the stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation. The most flexible neural circuits are circuits that can learn. Despite extensive theoretical work on biologically plausible learning rules, however, it has been difficult to obtain clear evidence about whether and how such rules are implemented in the brain. In the second part of this dissertation, I consider biologically plausible supervised- and reinforcement-learning rules and ask whether biased changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. I derive a metric to distinguish between learning rules by observing biased changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for perfect knowledge of this mapping, I focus on modeling a cursor-control BMI task using recurrent neural networks, and show that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.

Oscillations and rhythms are measured in the brain through large-scale measures like EEG (electroencephalogram) and LFP (Local Field Potential). Particularly, cortical gamma rhythms (30-90 Hz) found in different brain regions are correlated with different cognitive states. Despite vast differences in the range frequencies in gamma rhythms, the regions communicate to complete high-level tasks. One way in which this takes place is entrainment, where the postsynaptic group phase-lock to the rhythmic input from the presynaptic group (constant phase-shift). Mathematical models of the neurons and the neural networks are proposed to uncover the mechanisms behind experimentally observed phenomena. Most works have used homogeneous models of spiking networks. These simplified models provide a valuable understanding of neural dynamics. However, neural heterogeneity (variation in the neural or network parameters) has been experimentally observed and shown to have a non-trivial effect on many neural processes. Few studies have dealt with the role of different types of neural heterogeneity in the entrainment of a large network, and how it affects the frequency range the neural network entrains to. In this project, we aimed to show how different types of network heterogeneity affect the ability of the networks to entrain to gamma frequencies. We used the Pyramidal-Interneuronal Network Gamma (PING) model, a model consisting of excitatory pyramidal cells (E-cells) and inhibitory interneurons (I-cells) that are synaptically connected and generate gamma oscillations. We show that heterogeneity in the synaptic conductance from excitatory neurons to inhibitory neurons greatly increases the frequency range over which the network can entrain. The mechanism that allows this to happen requires the heterogeneity to 1. Create an I-cell excitability gradient; 2. Introduce input synchrony difference among the I-cells. The entrained I-cell subsets formed under these two conditions are necessary for well-enhanced entrainment as they support the entrainment of the whole network through feedback inhibition. This improvement is shown to be robust in large parameter space.

This thesis deals with the potential application of neural network technology to aviation fuel consumption estimation. This is achieved by developing neural networks representative jet aircraft. Fuel consumption information obtained directly from the pilot's flight manual was trained by the neural network. The trained network was able to accurately and efficiently estimate fuel consumption of an aircraft for a given mission. Statistical analysis was conducted to test the reliability of this model for all segments of flight. Since the neural network model does not require any wind tunnel testing nor extensive aircraft analysis, compared to existing models used in aviation simulation programs, this model shows good potential. The design of the model is described in depth, and the MATLAB source code are included in appendices. / Master of Science

The hippocampus has been the focus of memory research for decades. While the functional role of this structure is not fully understood, it is widely recognized as being vital for rapid yet accurate encoding and retrieval of associative memories. Since the discovery of adult hippocampal neurogenesis (AHN) in the dentate gyrus (DG) by Altman and Das in the 1960s, many theories and models have been formulated to explain the functional role it plays in learning and memory. These models postulate different ways in which new neurons are introduced into the DG and their functional importance for learning and memory. Few, if any, previous models have incorporated the unique properties of young adult-born dentate granule cells (DGCs) and their developmental trajectory. In this thesis, we propose a novel computational model of the DG that incorporates the developmental trajectory of these DGCs, including changes in synaptic plasticity, connectivity, excitability and lateral inhibition, using a modified version of the restricted boltzmann machine (RBM). Our results show superior performance on memory reconstruction tasks for both recent and distally learned items, when the unique characteristics of young DGCs are taken into account. The unique properties of the young neurons contribute to reducing retroactive and proactive interference, at both short and long time scales, despite the reduction in pattern separation due to their hyperexcitability. Our replacement model is subsequently extended to support learning dependent regulation of neurogenesis and apoptosis, using a convergence based approach to network growing and pruning. This hybrid additive and replacement model provides a more realistic and flexible approach to investigating the role of neurogenesis regulation in learning and memory. Finally, we incorporate the dentate gyrus model into a full hippocampal circuit to assess cued recall performance. Once again, our neurogenesis model shows decreased proactive and retroactive interference. / Thesis / Master of Science (MSc)

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To understand remotely sensed data, one must understand the relationship between radiative transfer models and their predictions of the interaction of solar radiation on geophysical media. If it can be established that these models are indeed accurate, some form of evaluation has to be performed on these models, for users to choose the model that suits their requirements. This thesis focuses on the implementation of a variable linear kernel model, its validation, and to study its application in the prediction of BRDF effects using two different neural networks-- the backpropogation and the radial basis function neural network and finally to draw conclusions on which neural network is best suited for this model. Based on these results the optimum number of kernels for this model is derived.