Computational Neuroscience Commons^™

Development Of A Neural Network Model To Identify Abnormalities In Cervical X-Rays, Alex P. Sheppert, Nasif Islam, Race Peterson, Michael T. Sullivan, John A. Kriak, David W. Sant, Kyle B. Bills

Annual Research Symposium

No abstract provided.

Analysis Of Electrophysiological Markers And Correlated Components Of Neural Responses To Discourse Coherence, Kurt M. Masiello

Dissertations, Theses, and Capstone Projects

Constructing meaning from spoken language is invaluable for learning, social interaction, and communication. In clinical populations with developmental disorders of speech comprehension, the severity of disruption can persist and vary from limiting occupational opportunities to lower performance outcomes. Previous research has reported an event-related potential (ERP) neural positivity over right hemisphere lateral anterior sites in response to semantic and discourse processing. Although useful as a marker for clinical populations of autism spectrum disorder (ASD) and developmental language disorder (DLD), little is understood about the dynamics and neural sources of this biological marker. In addition to traditional methods of ERP analysis, …

Microtubule Polarity Flaws As A Treatable Driver Of Neurodegeneration, Bridie D. Eckel, Roy Cruz Jr., Erin M. Craig, Peter W. Baas

All Faculty Scholarship for the College of the Sciences

Microtubule disruption is a common downstream mechanism leading to axonal degeneration in a number of neurological diseases. To date, most studies on this topic have focused on the loss of microtubule mass from the axon, as well as changes in the stability properties of the microtubules and/or their tubulin composition. Here we posit corruption of the normal pattern of microtubule polarity orientation as an underappreciated and yet treatable contributor to axonal degeneration. We include computational modeling to fortify the rigor of our considerations. Our simulations demonstrate that even a small deviation from the usual polarity pattern of axonal microtubules is …

Using Machine Learning To Identify Neural Mechanisms Underlying The Development Of Cognition In Children And Adolescents With Adhd, Brian Pho

Electronic Thesis and Dissertation Repository

Childhood and adolescence are marked by improvements to cognition and by the emergence of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). What neural mechanisms are associated with cognitive development in ADHD? In this study, I applied machine learning models to functional connectivity profiles to identify patterns of network connectivity that predict various cognitive abilities in a group of participants ages 6 to 16 with ADHD. The models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not adolescents. Furthermore, the models identified connections with the default mode, memory retrieval, and …

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

The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences 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, …

Improving An Ssvep-Based Brain Computer Interface Speller, Mac Kenzie J. Frank

Honors Theses

A brain-computer interface (BCI) is a novel technology that creates direct assistive communication between the brain and a computer. While numerous electroencephalogram (EEG) based BCI-speller applications have been used for communication by adults with physical disabilities; few BCI studies have included children, and none using BCI spellers. A pilot study of a developmentally-appropriate EEG-based speller-storybook interface that relied on steady-state visual evoked potentials (SSVEPs) by two pediatric users with quadriplegic cerebral palsy showed limited speller reliability (E. Floreani, personal communication, September 30, 2021). In the pilot study, the alphabet was parsed between three boxes, each flashing at a different rate …

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 …

A Two-Layer Model Explains Higher-Order Feature Selectivity Of V2 Neurons, Timothy D. Oleskiw, Justin D. Lieber, J. Anthony Movshon, Eero P. Simoncelli

MODVIS Workshop

Neurons in cortical area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture. However, a functional account of how V2 neurons build selectivity for complex natural image features from their inputs – V1 neurons locally tuned for orientation and spatial frequency – remains elusive.

We made single-unit recordings in area V2 in two fixating rhesus macaques. We presented stimuli composed of multiple superimposed grating patches that localize contrast energy in space, orientation, and scale. V2 activity is modeled via a two-layer linear-nonlinear network, optimized to use a sparse combination of V1-like outputs to account …

A Bayesian Account Of Depth From Shadow, James Elder, Patrick Cavanagh, Roberto Casati

MODVIS Workshop

When an object casts a shadow on a background surface, the offset of the shadow can be a compelling cue to the relative depth between the object and the background (e.g., Kersten et al 1996, Fig. 1). Cavanagh et al (2021) found that, at least for small shadow offsets, perceived depth scales almost linearly with shadow offset. Here we ask whether this finding can be understood quantitatively in terms of Bayesian decision theory.

Estimating relative depth from shadow offset is complicated by the fact that the shadow offset is co-determined by the slant of the light source relative to the …