Computational Neuroscience Commons^™

A Generalized Framework For The “Core” Thalamocortical Circuit Computation, Charles Liu

Dartmouth College Ph.D Dissertations

This thesis explores a biologically inspired computational framework based on a common excitatory-inhibitory circuit in the superficial layers of the cortex. By varying the strength of local feedback inhibition, this single circuit gives rise to distinct unsupervised learning behaviors: strong inhibition leads to clustering and hierarchical clustering, while weak inhibition produces principal component-like representations. From this principle, we develop three algorithms: Lateral Inhibition Clustering (LI-C), Lateral Inhibition Hierarchical Clustering (LI-HC), and Antipodal Iterative Mean Estimation (AIME).

LI-C performs clustering without requiring a predefined number of clusters, using a competitive learning rule to extract dominant patterns from data. LI-HC extends this …

Behavioral And Neural Evidence For Perceptual Inference Of Position, Sharif Saleki

Dartmouth College Ph.D Dissertations

Extensive neuroscientific research has advanced our knowledge of the mechanisms involved in the detection and recognition of visual stimuli. However, to guide flexible behavior in a changing world, the brain must also construct stable and coherent perceptual representations from retinal images that are often ambiguous. To resolve ambiguities and uncertainties, the visual system combines its input with other sources of information such as context and prior knowledge to infer the most likely interpretation of a scene. Thus, perception is inherently an inferential process that goes beyond simple detection or recognition of stimuli. Although inference has been studied in many areas …

Robust And Generalizable Representations In The Hippocampus, Hung-Tu Chen

Dartmouth College Ph.D Dissertations

An intelligent system must balance generalizing across similar experiences with maintaining the distinctiveness of each experience. This thesis explores how the hippocampus manages this balance through its neural representations to support adaptive behavior. In Chapter 1, I provide an overview of key hippocampal phenomena that contribute to this process, including remapping, splitter signal, and replay. In Chapter 2, I challenge the concept of random remapping by showing that it is possible to predict, better than chance, how a given experience will be encoded in the hippocampus across different subjects. This suggests that encoding of related experiences, which was previously thought …

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 …

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen

Dartmouth College Ph.D Dissertations

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …

Unraveling The Neural Basis Of Emotions: Advancing Understanding With Ecologically Valid Paradigms And High-Resolution Intracranial Eeg, Tiankang Xie

Dartmouth College Ph.D Dissertations

Background

Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life …

Accelerated Forgetting In People With Epilepsy: Pathologic Memory Loss, Its Neural Basis, And Potential Therapies, Sarah Ashley Steimel Phd

Dartmouth College Ph.D Dissertations

While forgetting is vital to human functioning, delineating between normative and disordered forgetting can become incredibly complex. This thesis characterizes a pathologic form of forgetting in epilepsy, identifies a neural basis, and investigates the potential of stimulation as a therapeutic tool. Chapter 2 presents a behavioral characterization of the time course of Accelerated Long-Term Forgetting (ALF) in people with epilepsy (PWE). This chapter shows evidence of ALF on a shorter time scale than previous studies, with a differential impact on recall and recognition. Chapter 3 builds upon the work in Chapter 2 by extending ALF time points and investigating the …

Beyond Homogenous Decision-Making Models: Role Of Brain Areas Interaction And Heterogeneity, Mohsen Rakhshan

Dartmouth College Ph.D Dissertations

Most existing mechanistic models of decision making consider microcircuits within a homogenous brain area and fail to consider the distributed nature of this cognitive process. More specifically, these models do not take into account: a) how different types of information might influence choice in different stages of the process; and b) how interactions and the heterogeneity between circuits across different brain areas might be essential for the process. In this dissertation, I present a combination of experimental and modeling approaches to study distributed nature of the decision-making by examining both the distributed processing of information as well as the interaction …