Computational Neuroscience Commons^™

Early Onset Alzheimer’S Disease Markers In Mouse Hippocampus Unveiled By Single-Cell Transcriptomic Analysis Following Cranial Radiotherapy, Tuba Aksoy

Dissertations and Theses (Open Access)

Cranial radiation therapy plays an integral role in the treatment of brain tumors but can lead to progressive cognitive deficits in survivors by mechanisms that are poorly understood. To develop preventive or mitigative strategies, it is crucial to better understand the underlying pathogenesis of radiation-induced cognitive impairments. The study investigated single-cell transcriptomics and DNA methylation changes as potential drivers of persistent cellular dysfunction after radiation exposure, specifically concentrating on the CA1-3 regions of the hippocampus and the prefrontal cortex due to their role in cognitive functions. Thirteen-week-old mice underwent whole-brain radiation at clinically relevant doses. Following whole-brain radiation, an assessment …

Improved Efficiency And Sensitivity Analysis Of 3-D Agent-Based Model For Pain-Related Neural Activity In The Amygdala, Kayla Kraeuter, Carley Reith, Benedict J. Kolber, Rachael Miller Neilan

Spora: A Journal of Biomathematics

Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model’s usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed …

Astrocyte Spatial Distribution Affects Growth Dynamics Of Breast Cancer Brain Metastases: An Agent-Based Modeling Study, Rupleen Kaur

Biology and Medicine Through Mathematics Conference

No abstract provided.

Fusing Classic Motion Energy Models And Deep Learning For Coarse-To-Fine Moving Object Segmentation, Matthias Tangemann, Matthias Kümmerer, Matthias Bethge

MODVIS Workshop

Classic motion energy models are able to predict a wide range of physiological and behavioral aspects of motion perception in humans. Whether these models can be used as a basis for higher-level tasks, such as moving object segmentation, has however hardly been explored yet. Here, we present a model that combines a motion energy representation with recent computer vision approaches for figure-ground segmentation of naturalistic stimuli. We find that unlike established motion segmentation models but similar to humans, our model generalizes to random-dot stimuli when only trained on RGB videos.

Perceptual Grouping With Latent Noise, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Humans effortlessly group elements into objects and segment them from the background and other objects without supervision. For example, the black and white stripes of a zebra are grouped together despite vastly different colors. A thorough theoretical and empirical account of perceptual grouping is still missing – Deep Neural Networks (DNNs), which are considered leading models of the visual system still regularly fail at simplistic perceptual grouping tasks. Here, we propose a counterintuitive unsupervised computational approach to perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We show that adding noise in …

Facilitation In Pattern Motion Perception Of Self-Operated Stimuli Explained By Adaptive Contrast Normalization, Fulvio Missoni, Francesca Peveri, Andrea Canessa, Giulia Sedda, Vittorio Sanguineti, Silvio P. Sabatini

MODVIS Workshop

Movement can affect the way we make sense of complex visual information. To investigate this issue, we designed an experiment to assess changes of plaid motion perception threshold after a period of sensorimotor contingency experience. We found that movement training facilitates combination of elementary motion cues into a global motion percept. No changes in perceptual thresholds are observed in a passive visual condition. A Bayesian model suggested a reduction, after training, of the cross-talk between two gratings with unbalanced contrasts in corresponding sensory channels. To test plausible neural mechanisms for active reduction of cross-talk in cortical representation of complex visual …

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.

Normalization With Dynamic Weights Predicts Neural And Behavioral Effects Of Orientation Adaptation, Huseyin Boyaci

MODVIS Workshop

Prolonged exposure to an oriented contour causes adaptation and has nontrivial effects on neural activity and perception. For example, the neuron's response amplitude may change (suppression or facilitation), the width of its tuning curve may change (broadening or sharpening), and its preferred orientation may shift (repulsion or attraction). Perceptually, adaptation affects the perceived orientation of a subsequently presented contour (direct and indirect tilt aftereffect) and alters orientation discrimination thresholds. In this study, I show that the normalization model with dynamic weights can predict these empirical results.

Local Geometry Of Elementary Visual Computations, Peter Neri

MODVIS Workshop

Visual operators (e.g. edge detectors) are classically modelled using small circuits involving canonical computations, such as template-matching and gain control. Circuit models explain many aspects of the empirical descriptors that are used to characterize local visual operators, from sensitivity to classification images. Notwithstanding their utility, these models fail to provide a unified framework encompassing the variety of effects observed experimentally, such as the impact of contrast, SNR, and attention on the above descriptors. My goal is to start with a simple, plausible geometrical representation of the perceptual operation carried out by the observer, and to show that this representation is …

Do Mechanisms Of Sinusoidal Contrast Sensitivity Account For Edge Sensitivity?, Lynn Schmittwilken, Felix A. Wichmann, Marianne Maertens

MODVIS Workshop

No abstract provided.

Spike Timing-Dependent Plasticity And Synaptic Scaling Invoke Episodic Bursting In An Excitatory Recurrent Neuronal Population, Ryno Chen, Gregory D. Conradi Smith

Biology and Medicine Through Mathematics Conference

No abstract provided.

Characterization Of Perceptual Spaces From Triadic Similarity Judgments, Jonathan Victor

MODVIS Workshop

Perceptual spaces are mental workspaces that organize a sensory or cognitive domain into a format that supports functions such as comparison, grouping, learning, and generalization. Determining the geometry of a perceptual space – i.e., the properties of the perceptual distances within the space -- is thus crucial not only to understand these intermediate levels of processing at an algorithmic level, but also as a starting point for comparison with neural measures of similarity. While perceptual spaces are often taken to be Euclidean or nearly so (the classic example is trichromatic color space), this assumption, when tested, is often violated. Moreover, …

A Foveated Model Of Visual Discrimination Based On Windowed Texture Statistics, Jan W. Kurzawski, William F. Broderick, Eero P. Simoncelli, Jonathan Winawer

MODVIS Workshop

Most information from visual scenes is discarded by the human nervous system and thus cannot influence visual behavior. Here, we investigated the loss of spatial pattern information. This loss increases dramatically with eccentricity. Recently, models have been developed that average image features in pooling windows whose diameters scale with eccentricity. These models can be used to synthesize “metamers” of natural scenes, images which are physically different but perceptually indistinguishable. Psychophysical experiments have identified the maximum window scaling for which model discrimination abilities match human performance (“critical scaling”). If images are synthesized with pooling windows that exceed critical scaling (“supercritical scaling”), …

Dissecting Bayes: Using Influence Measures To Test Normative Use Of Probability Density Information Derived From A Sample, Laurence T. Maloney, Keiji Ota

MODVIS Workshop

Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings or overall percent correct) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary …

Explaining The Staircase Gelb Illusion, Simultaneous Contrast, And Perceptual Fading Of Stabilized Images With A Neural Model Driven By Fixational Eye Movements, Michael E. Rudd

MODVIS Workshop

A neural model of lightness computation driven by fixational eye movements is described and used to simulate various lightness phenomenon, including the Staircase Gelb illusion and its variants, simultaneous contrast, the Chevreul illusion, and perceptual fading of stabilized images. The model provides a precise account of the lightness matches from several experiments, with an overall error of only 1.5%. In the model, spatial maps of transient ON and OFF cell activations—produced as the eyes traverse the visual scene—are sorted by eye movement direction in visual cortex. At a subsequent processing stage, the activations within these maps are summed across space …

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 …

Mapping The Neural Circuits That Underlie Metabolic Vs. Emotional Regulation Of Food-Seeking Behavior, Xu Zhang

Dissertations and Theses (Open Access)

Flexibly adjusting food-seeking behaviors based on metabolic needs and environmental threats is crucial for animal survival. In humans, maladaptive food-seeking behaviors that contravene energy homeostasis, which is often driven by food-associated cues with hedonic reward values, lead to obesity and eating disorders. However, the neural mechanisms underlying the modulation of cued food-seeking behaviors by distinct metabolic and threat states remain elusive. Using an approach-food vs. avoid-predator threat conflict test in rats, we identified a subpopulation of neurons in the anterior portion of the paraventricular thalamic nucleus (aPVT) which express corticotrophin-releasing factor (CRF) and are preferentially recruited to respond to food …

Biophysical Model Of Retraction Motor Neurons And Their Modification By Operant Conditioning, Maria Rasheed

Dissertations and Theses (Open Access)

Operant conditioning (OC) is a form of associative learning in which an animal modifies its behavior based on the consequences that follow that behavior. Despite its ubiquity, the underlying mechanisms of OC are poorly understood. Insights into the mechanisms of OC can be obtained by studying Aplysia feeding behavior as it can be modified by OC. This behavior is mediated by a central pattern generator (CPG) network in the buccal ganglia that contains a relatively small number of neurons. This CPG generates rhythmic motor patterns (BMPs) that move food into the gut by closing a tongue-like structure (i.e., radula) during …

The Genomics Of Champ1: Insights Into Their Cell-Type Specificity And Developmental Trajectories, Zoe Marie Van Caugherty

MUSC Theses and Dissertations

Chromosome alignment maintaining phosphoprotein 1(CHAMP1) is a gene that encodes a zinc finger protein that is involved in in the maintenance of kinetochore-microtubule attachment and regulating chromosome segregation in mitosis. (Itoh et al., 2011) CHAMP1 mutations have been shown to be major risk factors for neurodevelopmental disorders (NDDs) and autism spectrum disorder (ASD).(Asakura et al., 2021; Isidor et al., 2016; Levy et al., 2022) Although there is information on the link between CHAMP1 mutations and NDD, the role of CHAMP1 in regulating processes of human cortical development, namely, neurogenesis, proliferation, and electrophysiological properties of newly born neurons, is unknown. This …

Elucidating Neuroinflammation In Multiple Sclerosis By Network Analysis, Nora C. Welsh

Dartmouth College Ph.D Dissertations

Multiple sclerosis (MS) is a heterogeneous disease, differing on many variables, including disease course, sex, and overall activity. Key characteristics of the disease encompass demyelination, axonal damage, neuronal loss, glial cell activation, and the infiltration of peripheral immune cells. Molecular proxies of these functions are secreted proteins, including cytokines and immunoglobulins, which, in the central nervous system (CNS), can be secreted into the cerebrospinal fluid (CSF). A detailed analysis of these secreted proteins can offer insights into the evolving immunological and neurodegenerative features as the disease progresses. To understand the dynamic biological processes involved in MS, I used network analysis …