Physical Sciences and Mathematics Commons^™

A Model Of Shell Structure And Pattern In Mollusks, Rahnuma Islam, Bard Ermentrout, Sabrina Streipert

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling Mechanisms Of Microtubule Dynamics And Polarity In Neurons, Anna Nelson, Veronica Ciocanel, Scott Mckinley, Hannah Scanlon

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling Human Temporal Eeg Responses To Vr Visual Stimuli, Richard R. Foster, Connor Delaney, Dean J. Krusienski, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.

The Neural Dynamics Of Autism Spectrum Disorder And Typically Developing Individuals: Insights From Eeg Modeling And Mathematical Analysis, Sungwoo Ahn, Evie A. Malaia, Leonid L. Rubchinsky

Biology and Medicine Through Mathematics Conference

No abstract provided.

Assessing The Pre- And Post-Synaptic Effects Of Opioids On Inspiratory Rhythmogenesis, Diego F. Morandi Zerpa, Jingzhi Zhao

Biology and Medicine Through Mathematics Conference

No abstract provided.

Analysis And Computation Of Constrained Sparse Coding On Emerging Non-Von Neumann Devices, Kyle Henke

Mathematics & Statistics ETDs

This dissertation seeks to understand how different formulations of the neurally inspired Locally Competitive Algorithm (LCA) represent and solve optimization problems. By studying these networks mathematically through the lens of dynamical and gradient systems, the goal is to discern how neural computations converge and link this knowledge to theoretical neuroscience and artificial intelligence (AI). Both classical computers and advanced emerging hardware are employed in this study. The contributions of this work include:

1. Theoretical Work: A comprehensive convergence analysis for networks using both generic Rectified Linear Unit (ReLU) and Rectified Sigmoid activation functions. Exploration of techniques to address the binary …

Analysis Of Nonsmooth Neural Mass Models, Cadi Howell

Honors College

Neural activity in the brain involves a series of action potentials that represent “all or nothing” impulses. This implies the action potential will only “fire” if the mem- brane potential is at or above a specific threshold. The Wilson-Cowan neural mass model [6, 28] is a popular mathematical model in neuroscience that groups excita- tory and inhibitory neural populations and models their communication. Within the model, the on/off behavior of the firing rate is typically modeled by a smooth sigmoid curve. However, a piecewise-linear (PWL) firing rate function has been considered in the Wilson-Cowan model in the literature (e.g., see …

A Causal Inference Approach For Spike Train Interactions, Zach Saccomano

Dissertations, Theses, and Capstone Projects

Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …

Neural Tabula Rasa: Foundations For Realistic Memories And Learning, Patrick R. Perrine

Master's Theses

Understanding how neural systems perform memorization and inductive learning tasks are of key interest in the field of computational neuroscience. Similarly, inductive learning tasks are the focus within the field of machine learning, which has seen rapid growth and innovation utilizing feedforward neural networks. However, there have also been concerns regarding the precipitous nature of such efforts, specifically in the area of deep learning. As a result, we revisit the foundation of the artificial neural network to better incorporate current knowledge of the brain from computational neuroscience. More specifically, a random graph was chosen to model a neural system. This …

The Mandelbrot Set For Networks, Templates And Mutated Systems, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Intermittent Synchronization In Gamma-Band Neural Oscillations, Anh Nguyen, Leonid L. Rubchinsky

Biology and Medicine Through Mathematics Conference

No abstract provided.

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska

Karbala International Journal of Modern Science

Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.

Mathematical Model Of Subthalamic Nucleus Neuron - Characteristic Activity Patterns And Bifurcation Analysis, Choongseok Park, Sungwoo Ahn, Leonid Rubchinsky

Biology and Medicine Through Mathematics Conference

No abstract provided.

Computational Model Of Mutant Arpp Protein Aggregation And Diffusion And Its Impact On Calcium Dynamics And Stress Responses Using Neuron., Gaby Clarke

Biology and Medicine Through Mathematics Conference

No abstract provided.

Estimating Glutamate Transporter Surface Density In Mouse Hippocampal Astrocytes, Anca R. Radulescu, Annalisa Scimemi

Biology and Medicine Through Mathematics Conference

No abstract provided.

Scale-Free Properties Influence Breathing Rhythmogenesis., Cameron Grover

Biology and Medicine Through Mathematics Conference

No abstract provided.

Automated Fitting Of Allosteric Parameters In Receptor Oligomer Models, Spenser Wood

Biology and Medicine Through Mathematics Conference

No abstract provided.

Olfactory Bulb Processing Of Ortho Versus Retronasal Odors, Michelle F. Craft, Andrea Barreiro, Shree Gautam, Woodrow Shew, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.

Noise As A Strategy, Nour Riman, Bard Ermentrout

Biology and Medicine Through Mathematics Conference

No abstract provided.

Empirical And Computational Approaches To Explore The Underlying Neuromodulatory Mechanisms Of Social Status Regulation On Zebrafish Motor Circuits, Sungwoo Ahn, Choongseok Park, Fadi Issa

Biology and Medicine Through Mathematics Conference

No abstract provided.

Probability Distributions Of Active Sensing, Kathleen Hoffman

Biology and Medicine Through Mathematics Conference

No abstract provided.

Scale-Free Behavioral Dynamics Directly Linked With Scale-Free Cortical Dynamics, Sabrina Jones

Physics Undergraduate Honors Theses

In organisms, an interesting phenomenon occurs in both behavior and neuronal activity: organization with fractal, scale-free fluctuations over multiple spatiotemporal orders of magnitude (1,2). In regard to behavior, this sort of complex structure-- which manifests itself from small scale fidgeting to purposeful, full body movements-- may support goals such as foraging (3-6), visual search (4), and decision making (7,8). Likewise, the presence of this sort of structure in the cerebral cortex in the form of spatiotemporal cascades, coined “neuronal avalanches,” may offer optimal information transfer (9). Thus, when considering the functional relationship between the cerebral cortex and movements of the …

Medical Schools Ignore The Nature Of Consciousness At Great Cost, Anoop Kumar

Journal of Wellness

The essential question of the relationship between consciousness and matter is ignored in medical school curricula, leading to a machine-like view of the human being that contributes to physician burnout and intellectual dissatisfaction. The evidence suggesting that the brain may not be the seat of consciousness is generally ignored to preserve the worldview of the primacy of matter. By investigating new frameworks detailing the nature of consciousness at different levels of hierarchy, we can bring intellectual rigor to a once opaque subject that supports a fundamental reality about our experience: We are human beings, not only human bodies.

Data Assimilation For Conductance-Based Neuronal Models, Matthew Moye

Dissertations

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the …

Oscillations Via Excitable Cells, Derek Orr, Bard Ermentrout

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling Of Pmv Neuronal Circuitry In Adaptive Changes Of Energy Balance, Pilhwa Lee, Cristina Saenz De Miera, Nicole Bellefontaine, Kevin W. Williams, Renata Frazao, Carol Elias

Biology and Medicine Through Mathematics Conference

No abstract provided.

Geometry-Based Estimates Of Glutamate Transporter Density In Astrocytes, Anca R. Radulescu, Cassandra Williams, Annalisa Scimemi

Biology and Medicine Through Mathematics Conference

No abstract provided.

Mechanisms Of Value-Biased Prioritization In Fast Sensorimotor Decision Making, Kivilcim Afacan-Seref

Dissertations and Theses

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively, but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. This research examines the behavioral and electrophysiological indices of how value biases split-second perceptual decisions and the possible mechanisms underlying the process. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases …

Socially Regulated Neural Circuits Activation Via Endocannabinoid And Dopaminergic Signaling In Zebrafish, Choongseok Park

Biology and Medicine Through Mathematics Conference

No abstract provided.