Open Access. Powered by Scholars. Published by Universities.®
Neuroscience and Neurobiology Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Purdue University (9)
- Western University (7)
- West Virginia University (6)
- Dartmouth College (3)
- Medical University of South Carolina (2)
-
- New Jersey Institute of Technology (2)
- Virginia Commonwealth University (2)
- California Polytechnic State University, San Luis Obispo (1)
- Central Washington University (1)
- City University of New York (CUNY) (1)
- Claremont Colleges (1)
- Illinois State University (1)
- Rhode Island School of Design (1)
- Roseman University of Health Sciences (1)
- The University of Maine (1)
- The University of Southern Mississippi (1)
- University of Kentucky (1)
- University of Massachusetts Amherst (1)
- University of New Mexico (1)
- University of Tennessee, Knoxville (1)
- Keyword
-
- Neuroscience (4)
- Attention (3)
- Computational neuroscience (3)
- Deep learning (3)
- EEG (3)
-
- Active vision (2)
- Feedback (2)
- Fixational eye movements (2)
- Hippocampus (2)
- Machine Learning (2)
- Memory (2)
- Neural coding (2)
- Neuroethology (2)
- Self supervised (2)
- Spatial vision (2)
- ANNs (1)
- ASD (1)
- Accelerated long-term forgetting (1)
- Aging (1)
- Artificial Light at Night (1)
- Artificial dendrites (1)
- Artificial intelligence (1)
- Artificial neural network (1)
- Attention Deficits (1)
- Attentional Blink (1)
- Autism (1)
- Autism Spectrum Disorder (1)
- Autism spectrum disorder (ASD) (1)
- Autism spectrum disorder (ASD) (1)
- Axon (1)
- Publication
-
- MODVIS Workshop (9)
- Electronic Thesis and Dissertation Repository (7)
- Graduate Theses, Dissertations, and Problem Reports (6)
- Dartmouth College Ph.D Dissertations (3)
- Biology and Medicine Through Mathematics Conference (2)
-
- Dissertations (2)
- Doctoral Dissertations (2)
- MUSC Theses and Dissertations (2)
- Master's Theses (2)
- All Faculty Scholarship for the College of the Sciences (1)
- Annual Research Symposium (1)
- Annual Symposium on Biomathematics and Ecology Education and Research (1)
- Dissertations, Theses, and Capstone Projects (1)
- Electrical and Computer Engineering ETDs (1)
- Electronic Theses and Dissertations (1)
- Masters Theses (1)
- Pitzer Senior Theses (1)
- Theses and Dissertations--Pharmacology and Nutritional Sciences (1)
- Publication Type
Articles 1 - 30 of 44
Full-Text Articles in Neuroscience and Neurobiology
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 …
The Effects Of Brain Control: A 3-D Agent-Based Model For Studying Pain, Kayla Kraeuter, Carley Reith, Benedict Kolber, Rachael Miller Neilan
The Effects Of Brain Control: A 3-D Agent-Based Model For Studying Pain, Kayla Kraeuter, Carley Reith, Benedict Kolber, Rachael Miller Neilan
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Invariant Object Recognition In Deep Neural Networks And Humans, Haider Al-Tahan
Invariant Object Recognition In Deep Neural Networks And Humans, Haider Al-Tahan
Electronic Thesis and Dissertation Repository
Invariant object recognition, a cornerstone of human vision, enables recognizing objects despite variations in rotations, positions, and scales. To emulate human-like generalization across object transformations, computational models must perform well in this aspect. Deep neural networks (DNNs) are popular models for human ventral visual stream processing, though their alignment with human performance remains inconsistent. We examine object recognition across transformations in human adults and pretrained feedforward DNNs. DNNs are grouped in model families by architecture, visual diet, and learning goal. We focus on object rotation in depth, and observe that object recognition performance is better preserved in humans than in …
Selective Recruitment Of Cerebellum In Cognition, Ladan Shahshahani
Selective Recruitment Of Cerebellum In Cognition, Ladan Shahshahani
Electronic Thesis and Dissertation Repository
Previous studies of cerebellar function in humans have shown that it is activated by a myriad of tasks ranging from motor learning and language to working memory and more. These studies have prompted a deviation from the traditional view of the cerebellum as a purely motor structure. However, the precise contribution of the cerebellum to these tasks remains ambiguous.
A prevalent assumption in fMRI studies is interpreting BOLD activation as evidence of the cerebellum's involvement in specific tasks. However, this interpretation is potentially misleading, especially considering that the BOLD signal predominantly represents cerebellar input, with output activity largely absent. Consequently, …
Visual Cortical Traveling Waves: From Spontaneous Spiking Populations To Stimulus-Evoked Models Of Short-Term Prediction, Gabriel B. Benigno
Visual Cortical Traveling Waves: From Spontaneous Spiking Populations To Stimulus-Evoked Models Of Short-Term Prediction, Gabriel B. Benigno
Electronic Thesis and Dissertation Repository
Thanks to recent advances in neurotechnology, waves of activity sweeping across entire cortical regions are now routinely observed. Moreover, these waves have been found to impact neural responses as well as perception, and the responses themselves are found to be structured as traveling waves. How exactly do these waves arise? Do they confer any computational advantages? These traveling waves represent an opportunity for an expanded theory of neural computation, in which their dynamic local network activity may complement the moment-to-moment variability of our sensory experience.
This thesis aims to help uncover the origin and role of traveling waves in the …
Neural Dynamics Of Visual Processes In Challenging Visibility Conditions, Saba Charmi Motlagh
Neural Dynamics Of Visual Processes In Challenging Visibility Conditions, Saba Charmi Motlagh
Electronic Thesis and Dissertation Repository
In our daily visual experience, our brain effortlessly categorizes countless objects, enabling us to perceive and interpret the world around us. This core object recognition process is vital for our survival and adaptive behavior, allowing us to recognize objects despite variations in appearance. The incredible speed at which we accomplish this task is a testament to the efficiency of our visual system and the significance of visual processing is evident in the allocation of nearly half of the neocortex in primates to this function. Unraveling the intricacies of how the human visual system tackles this complex challenge has long been …
Neural Dynamics Of Target Processing In Attentional Blink, Mansoure Jahanian
Neural Dynamics Of Target Processing In Attentional Blink, Mansoure Jahanian
Electronic Thesis and Dissertation Repository
The attentional blink (AB) phenomenon refers to the failure to report the second target (T2) if it appears 200-500 ms after the first target (T1) in a stream of rapidly presented images. The present study aimed to investigate the neural representations of target processing under conditions where AB does or does not occur. We recorded EEG and behavioral data while participants viewed a rapid sequence of natural object images embedded with two face targets presented at two lag conditions: lag 3 (targets were 252 ms apart) and lag 7 (targets were 588 ms apart). Consistent with AB, our behavioral results …
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
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 …
Temporal Dynamics Of Natural Sound Categorization, Ali Tafakkor
Temporal Dynamics Of Natural Sound Categorization, Ali Tafakkor
Electronic Thesis and Dissertation Repository
While extensive research has elucidated the brain’s processing of semantics from speech sound waves and their mapping onto the auditory cortex, the temporal dynamics of how meaningful non-speech sounds are processed remain less examined. Understanding these dynamics is key to resolving the debate between cascaded and parallel hierarchical processing models, both plausible given the anatomical evidence. This study investigates how semantic category information from environmental sounds is processed in the temporal domain, using electroencephalography (EEG) collected from 25 participants and representational similarity analysis (RSA) along with models of acoustic and semantic information. We examined information extracted by the brain from …
Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Electronic Thesis and Dissertation Repository
Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.
To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …
The Consolidation Of Memory Associations, Kyle A. Kainec
The Consolidation Of Memory Associations, Kyle A. Kainec
Doctoral Dissertations
Creating memories is a fundamental challenge for the human brain. To create memories, defining features of experiences must be stored distinguishably without forgetting other memories. Memory associations represent co-occurring features and defining features across experiences. Memory associations are represented as networks of information that are stored in the brain. New memory associations are encoded during experiences and can be used to update existing memory associations during offline intervals. However, the mechanisms that underlie how encoded memory associations are stored within existing networks during offline intervals remains unclear. The experiments in this dissertation address a significant theoretical gap in understanding the …
Phenotyping Regression In A Female Mouse Model For Rett Syndrome Using Computational Neuroethology Tools, Michael J. Mykins
Phenotyping Regression In A Female Mouse Model For Rett Syndrome Using Computational Neuroethology Tools, Michael J. Mykins
Doctoral Dissertations
Regression is defined as loss of acquired skills over time and is a key feature of many neurodevelopmental disorders such as Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene Methyl CpG-Binding Protein 2 (MECP2) and is characterized by a period of typical development with subsequent regression of previously acquired motor and speech skills in girls. In human and animal models, it is clear syndromic phenotypes are dynamic over time but phenotyping regression over time in animal models has remained elusive. Lack of established timelines to study the molecular, cellular, and behavioral features of regression in female …
Solving The Cable Equation, A Second-Order Time Dependent Pde For Non-Ideal Cables With Action Potentials In The Mammalian Brain Using Kss Methods, Nirmohi Charbe
Master's Theses
In this thesis we shall perform the comparisons of a Krylov Subspace Spectral method with Forward Euler, Backward Euler and Crank-Nicolson to solve the Cable Equation. The Cable Equation measures action potentials in axons in a mammalian brain treated as an ideal cable in the first part of the study. We shall subject this problem to the further assumption of a non-ideal cable. Assume a non-uniform cross section area along the longitudinal axis. At the present time, the effects of torsion, curvature and material capacitance are ignored. There is particular interest to generalize the application of the PDEs including and …
Destined Failure, Chengjun Pan
Destined Failure, Chengjun Pan
Masters Theses
I attempt to examine the complex structure of human communication, explaining why it is bound to fail. By reproducing experienceable phenomena, I demonstrate how they can expose communication structure and reveal the limitations of our perception and symbolization.I divide the process of communication into six stages: input, detection, symbolization, dictionary, interpretation, and output. In this thesis, I examine the flaws and challenges that arise in the first five stages. I argue that reception acts as a filter and that understanding relies on a symbolic system that is full of redundancies. Therefore, every interpretation is destined to be a deviation.
Neural Tabula Rasa: Foundations For Realistic Memories And Learning, Patrick R. Perrine
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 …
Neural Correlates Of Post-Traumatic Brain Injury (Tbi) Attention Deficits In Children, Meng Cao
Neural Correlates Of Post-Traumatic Brain Injury (Tbi) Attention Deficits In Children, Meng Cao
Dissertations
Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can …
Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi
Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi
Dissertations
Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.
Chapter 1 provides background information on …
Gap Junctions And Synchronization Clusters In The Thalamic Reticular Nuclei, Anca R. Radulescu, Michael Anderson
Gap Junctions And Synchronization Clusters In The Thalamic Reticular Nuclei, Anca R. Radulescu, Michael Anderson
Biology and Medicine Through Mathematics Conference
No abstract provided.
Computing Brain Networks With Complex Dynamics, Anca R. Radulescu
Computing Brain Networks With Complex Dynamics, Anca R. Radulescu
Biology and Medicine Through Mathematics Conference
No abstract provided.
Task-Driven Influences On Fixational Eye Movements, Jonathan Victor, Yen-Chu Lin, Michele Rucci
Task-Driven Influences On Fixational Eye Movements, Jonathan Victor, Yen-Chu Lin, Michele Rucci
MODVIS Workshop
There is now compelling evidence that the spatiotemporal remapping carried out by fixational eye movements (FEMs) is an essential step in visual processing. Moreover, the overall Brownian-like statistics of FEMs are calibrated to map fine spatial detail into the temporal frequency range to which retinal circuitry is tuned. Here, we tested the hypothesis that the detailed spatial characteristics of FEMs can be adjusted to task demands via cognitive influences that operate even in the absence of a visual stimulus. We examined FEMs in a task that required subjects (N=6) to report which of two letters was displayed. Trials were blocked; …
Active Encoding Of Space Through Time, Michele Rucci, Jonathan D. Victor
Active Encoding Of Space Through Time, Michele Rucci, Jonathan D. Victor
MODVIS Workshop
No abstract provided.
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
MODVIS Workshop
No abstract provided.
Toward A Manifold Encoding Neural Responses, Luciano Dyballa, Andra M. Rudzite, Mahmood S. Hoseini, Mishek Thapa, Michael P. Stryker, Greg D. Field, Steven W. Zucker
Toward A Manifold Encoding Neural Responses, Luciano Dyballa, Andra M. Rudzite, Mahmood S. Hoseini, Mishek Thapa, Michael P. Stryker, Greg D. Field, Steven W. Zucker
MODVIS Workshop
Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields.
Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology …
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
MODVIS Workshop
We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.
From Image Gradients To A Perceptual Metric Space, Alan Johnston
From Image Gradients To A Perceptual Metric Space, Alan Johnston
MODVIS Workshop
How do we achieve a sense of spatial dimension from a sense of location? There are three predominant ideas about how we achieve this; spatial isomorphism, in which what we see reflects differences in distance or size in the brain; that spatial extent depends upon motor sensations or intentions related to eye movements; and that distance is computed from the degree of correlation in neural activity between adjacent locations, with distance inversely proportional to the correlation. There are problems with each of these approaches, for example, neural correlation may depend more on image structure than adjacency - consider the case …
V1 Saliency Hypothesis And Central-Peripheral Dichotomy (Cpd), Li Zhaoping Prof. Dr.
V1 Saliency Hypothesis And Central-Peripheral Dichotomy (Cpd), Li Zhaoping Prof. Dr.
MODVIS Workshop
No abstract provided.
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
MODVIS Workshop
Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …
Efficient Coding Of Local 2d Shape, James Elder, Timothy D. Oleskiw, Ingo Fruend, Gerick M. Lee, Andrew Sutter, Anitha Pasupathy, Eero Simoncelli, J Anthony Movshon, Lynne Kiorpes, Najib Majaj
Efficient Coding Of Local 2d Shape, James Elder, Timothy D. Oleskiw, Ingo Fruend, Gerick M. Lee, Andrew Sutter, Anitha Pasupathy, Eero Simoncelli, J Anthony Movshon, Lynne Kiorpes, Najib Majaj
MODVIS Workshop
Efficient coding provides a concise account of key early visual properties, but can it explain higher-level visual function such as shape perception? If curvature is a key primitive of local shape representation, efficient shape coding predicts that sensitivity of visual neurons should be determined by naturally-occurring curvature statistics, which follow a scale-invariant power-law distribution. To assess visual sensitivity to these power-law statistics, we developed a novel family of synthetic maximum-entropy shape stimuli that progressively match the local curvature statistics of natural shapes, but lack global structure. We find that humans can reliably identify natural shapes based on 4th and …
Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial
Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial
Electrical and Computer Engineering ETDs
Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain’s visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.
Artificial Dendritic Neuron: A Model Of Computation And Learning Algorithm, Zachary Hutchinson
Artificial Dendritic Neuron: A Model Of Computation And Learning Algorithm, Zachary Hutchinson
Electronic Theses and Dissertations
Dendrites are root-like extensions from the neuron cell body and have long been thought to serve as the predominant input structures of neurons. Since the early twentieth century, neuroscience research has attempted to define the dendrite’s contribution to neural computation and signal integration. This body of experimental and modeling research strongly indicates that dendrites are not just input structures but are crucial to neural processing. Dendritic processing consists of both active and passive elements that utilize the spatial, electrical and connective properties of the dendritic tree.
This work presents a neuron model based around the structure and properties of dendrites. …