Computational Mechanisms Of Face Perception, Jinge Wang

Graduate Theses, Dissertations, and Problem Reports (ETD)

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for …

Artificial Light At Night Disrupts Pain Behavior And Cerebrovascular Structure In Mice, Jacob Raymond Bumgarner

Graduate Theses, Dissertations, and Problem Reports (ETD)

Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice

Jacob R. Bumgarner

Circadian rhythms are intrinsic biological processes that fluctuate in function with a period of approximately 24 hours. These rhythms are precisely synchronized to the 24- hour day of the Earth by external rhythmic signaling cues. Solar light-dark cycles are the most potent environmental signaling cue for terrestrial organisms to align internal rhythms with the external day. Proper alignment and synchrony of internal circadian rhythms with external environmental rhythms are essential for health and optimal biological function.

The modern human environment on Earth is no longer …

Sense And Sensitivity: Spatial Structure Of Conspecific Signals During Social Interaction, Keshav Ramachandra

Graduate Theses, Dissertations, and Problem Reports (ETD)

Organisms rely on sensory systems to gather information about their environment. Localizing the source of a signal is key in guiding the behavior of the animal successfully. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. In this dissertation, I investigate the spatial dynamics of natural stimuli and explore how the electrosensory system of weakly electric fish encodes these realistic spatial signals. To do so In Chapter 2, I develop a model that examines the strength of the signal as it reaches the sensory array and simulates the responses of the receptors. The …

Spatial Processing Of Conspecific Signals In Weakly Electric Fish: From Sensory Image To Neural Population Coding, Oak Everette Milam

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this dissertation, I examine how an animal’s nervous system encodes spatially realistic conspecific signals in their environment and how the encoding mechanisms support behavioral sensitivity. I begin by modeling changes in the electrosensory signals exchanged by weakly electric fish in a social context. During this behavior, I estimate how the spatial structure of conspecific stimuli influences sensory responses at the electroreceptive periphery. I then quantify how space is represented in the hindbrain, specifically in the primary sensory area called the electrosensory lateral line lobe. I show that behavioral sensitivity is influenced by the heterogeneous properties of the pyramidal cell …

Spiking Neural Network That Maps From Generalized Coordinates To Cartesian Coordinates, Chloe K. Guie

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this thesis, I look to understand how insects compute task-level quantities by integrating range-fractionated sensory signals to create a sparse-spatial coding of Cartesian positions. I created biologically plausible 2-D and 3-D models of one species of the stick insect (Carausius morosus) leg and encoded the foot position through a spiking neural network. This model used spiking afferents from three angles of an insect leg which are integrated by one non-spiking interneuron. This model contains many dendritic compartments and one somatic compartment that encode the foot’s position relative to the body. The Functional Subnetwork Approach (FSA) was used …

Probing The Boundaries Of Human Agency, Sook Mun Wong

Computational and Data Sciences (PhD) Dissertations

In this dissertation, I aim to advance our understanding of human agency and how it is computed. First, I probe the limits of what behavior we can and cannot produce agentically. Secondly, I explore microdosing with psychedelic drugs as a method of perturbing and studying changes to the sense of agency. Thirdly, I use transcranial magnetic stimulation to induce body movements and investigate how the sense of agency is affected. Finally, I summarise my findings and synthesize insights to further our understanding human agency.

Causal Inference In Psychology And Neuroscience: From Association To Causation, Dehua Liang

Computational and Data Sciences (PhD) Dissertations

In psychology and neuroscience, inferring causality in non-experimental studies is almost taboo, because data in these studies, e.g., survey data and resting-state neuroimaging data, are often contaminated by unmeasured confounders. Psychologists and neuroscientists are often cautious about their results, and reluctant to make false claims about causality in non-experimental studies. Therefore, they adopt less stringent statistical analysis techniques that can only infer associational relations. However, the ambiguity about causality in traditional statistical analysis creates much confusion in interpreting analytical results - some studies make implicit causal claims about their results using words such as “impacts”, “lead to” and “affects”. This …

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%.

Analysis Of The Distributed Representation Of Operant Memory In Aplysia, Renan Murillo Costa

Dissertations and Theses (Open Access)

Operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior, engages a high-dimensional neuronal population space spanning multiple brain regions. A complete characterization of an operant memory remains elusive. Some sites of plasticity participating in the engram underlying an example of operant memory in Aplysia have been previously uncovered. Three studies are described here that sought to draw closer to a thorough characterization of this memory. The first study used a computational model to examine the ways in which sites of plasticity (individually and in combination) contribute to memory expression. Each site of plasticity …

Social Virtual Reality: Neurodivergence And Inclusivity In The Metaverse, James Hutson

Faculty Scholarship

Whereas traditional teaching environments encourage lively and engaged interaction and reward extrovert qualities, introverts, and others with symptoms that make social engagement difficult, such as autism spectrum disorder (ASD), are often disadvantaged. This population is often more engaged in quieter, low-key learning environments and often does not speak up and answer questions in traditional lecture-style classes. These individuals are often passed over in school and later in their careers for not speaking up and are assumed to not be as competent as their gregarious and outgoing colleagues. With the rise of the metaverse and democratization of virtual reality (VR) technology, …

Mixed Mode Oscillations In Three-Timescale Coupled Morris-Lecar Neurons, Ngocanh Phan, Yangyang Wang

Biology and Medicine Through Mathematics Conference

No abstract provided.

Universality And Synchronization In Complex Quadratic Networks (Cqns), Anca R. Radulescu, Danae Evans

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.

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.

Closed-Loop Brain-Computer Interfaces For Memory Restoration Using Deep Brain Stimulation, David Xiaoliang Wang

Electrical Engineering Theses and Dissertations

The past two decades have witnessed the rapid growth of therapeutic brain-computer interfaces (BCI) targeting a diversity of brain dysfunctions. Among many neurosurgical procedures, deep brain stimulation (DBS) with neuromodulation technique has emerged as a fruitful treatment for neurodegenerative disorders such as epilepsy, Parkinson's disease, post-traumatic amnesia, and Alzheimer's disease, as well as neuropsychiatric disorders such as depression, obsessive-compulsive disorder, and schizophrenia. In parallel to the open-loop neuromodulation strategies for neuromotor disorders, recent investigations have demonstrated the superior performance of closed-loop neuromodulation systems for memory-relevant disorders due to the more sophisticated underlying brain circuitry during cognitive processes. Our efforts are …

Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat

MODVIS Workshop

No abstract provided.

Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni

MODVIS Workshop

We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …

Characterization Of Local And Global Statistics In Three Kinds Of Medical Images, And An Example Of Their Role In A Clinical Judgment, Jonathan Victor, Amanda Simon, Craig K. Abbey

MODVIS Workshop

No abstract provided.

Individual Differences In Structure Learning, Philip Newlin

Theses and Dissertations

Humans have a tendency to impute structure spontaneously even in simple learning tasks, however the way they approach structure learning can vary drastically. The present study sought to determine why individuals learn structure differently. One hypothesized explanation for differences in structure learning is individual differences in cognitive control. Cognitive control allows individuals to maintain representations of a task and may interact with reinforcement learning systems. It was expected that individual differences in propensity to apply cognitive control, which shares component processes with hierarchical reinforcement learning, may explain how individuals learn structure differently in a simple structure learning task. Results showed …