Computational Neuroscience Commons^™

Appropriate Kernels For Divisive Normalization Explained By Wilson-Cowan Equations, Jesus Malo, Marcelo Bertalmio

MODVIS Workshop

Cascades of standard Linear+NonLinear-Divisive Normalization transforms [Carandini&Heeger12] can be easily fitted using the appropriate formulation introduced in [Martinez17a] to reproduce the perception of image distortion in naturalistic environments. However, consistently with [Rust&Movshon05], training the model in naturalistic environments does not guarantee the prediction of well known phenomena illustrated by artificial stimuli. For example, the cascade of Divisive Normalizations fitted with image quality databases has to be modified to include a variety aspects of masking of simple patterns. Specifically, the standard Gaussian kernels of [Watson&Solomon97] have to be augmented with extra weights [Martinez17b]. These can be introduced ad-hoc using the intuition …

Model Investigation On Contribution Of Feedback In Distortion Induced Motion Adaptation, Siegfried Wahl, Selam Habtegiorgis, Christian Jarvers, Katharina Rifai, Heiko Neumann

MODVIS Workshop

Motion information is processed in a neural circuit formed by synaptic organization of feedforward (FF) and feedback (FB) connections between different cortical areas. However, the contribution of a recurrent FB information to adaptation process is not well explored. Here, a biologically plausible neural model that predicts motion adaptation aftereffect (MAE) induced by exposure to geometrically skewed natural image sequences is suggested. The model constitutes two stage recurrent motion processing within cortical areas V1 and MT [1]. It comprises FF excitatory, FB modulatory and lateral inhibitory connections, and a fast and a slow adaptive synapse in the FF and FB streams, …

A Model Of 1d And 2d Motion Processing In The Primate Brain, Alan Johnston

MODVIS Workshop

Velocity encoding in the primate brain can be modelled by a spatiotemporal gradient approach, with neurons characterized as spatio-temporal derivative operators (Johnston et al. 1999). This strategy works well for moving 1D spatial patterns, but it can produce systematic errors, as it can be overly influenced by the direction of the local spatial gradient of the image brightness. For 2D pattern it is possible to develop a similar spatio-temporal approach, in which the system solves a set of over-determined linear equations directly, to provide an estimate for the 2D image motion. However, in this case the matrix one needs to …

An Active Efficient Coding Model Of The Development Of Amblyopia, Samuel Eckmann, Lukas Klimmasch, Bertram Shi, Jochen Triesch

MODVIS Workshop

No abstract provided.

Molecular Mechanism Of Early Amyloid Self-Assembly Revealed By Computational Modeling, Mohtadin Hashemi

Theses & Dissertations

Protein misfolding followed by the formation of aggregates, is an early step in the cascade of conformational changes in a protein that underlie the development of several neurodegenerative diseases, including Alzheimer’s and Parkinson’s diseases. Efforts aimed at understanding this process have produced little clarity and the mechanism remains elusive.

Here, we demonstrate that the hairpin fold, a structure found in the early folding intermediates of amyloid b, induces morphological and stability changes in the aggregates of Aβ(14-23) peptide. We structurally characterized the interactions of monomer and hairpin using extended molecular dynamics (MD) simulations, which revealed a novel intercalated type complex. …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Tinnitus And Dysfunctional Interactions Between Distributed Resting State Networks, Sivayini Kandeepan

Western Research Forum

It is known that peripheral lesions in the cochlea or the auditory nerve produce dysfunctional input to central auditory structures and induce changes in the auditory system causing tinnitus. Recently, it has been proposed that the unified percept of tinnitus could be considered as an emergent property of multiple overlapping dynamic brain networks, each encoding a specific tinnitus characteristic.

The aim of our study was to investigate the neuronal activation patterns associated with specific clinical tinnitus characteristics using fMRI. We hypothesize that tinnitus clinical characteristics could be associated with specific resting-state activity and connectivity patterns and that this could be …

Review: Do The Different Sensory Areas Within The Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent A Network Multisensory Hub?, M. Alex Meredith, Mark T. Wallace, H. Ruth Clemo

Anatomy and Neurobiology Publications

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian …

Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang

Journal of the Arkansas Academy of Science

The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.

The Rhetoric Of Science Education And Technology, Iwasan D. Kejawa

School of Computing: Faculty Publications

Nearly thousands of science experiments are performed both on humans and animals every year in the United States (Gregory, 1999). Does Science enormously play a role in the well-beings of individual in the society? Research has found that science education is through motivation and satisfying the needs of humans. The scientific world is part of an elongated human development. This can be substantiated with the use and evolution of TECHNOLOGY and SCIENCE (Minton, 2004). Education of the entities that comprise the need to achieve the goal of TECHNOLOGY and SCIENCE which are important issues of today. Research has shown that …

Effect Of Neuromodulation Of Short-Term Plasticity On Information Processing In Hippocampal Interneuron Synapses, Elham Bayat Mokhtari

Graduate Student Theses, Dissertations, & Professional Papers

Neurons convey information about the complex dynamic environment in the form of signals. Computational neuroscience provides a theoretical foundation toward enhancing our understanding of nervous system. The aim of this dissertation is to present techniques to study the brain and how it processes information in particular neurons in hippocampus.

We begin with a brief review of the history of neuroscience and biological background of basic neurons. To appreciate the importance of information theory, familiarity with the information theoretic basics is required, these basics are presented in Chapter 2. In Chapter 3, we use information theory to estimate the amount of …

Saccadic Eye Movements Between Strategic, Interceptive, And Non-Athletes, Brian Szekely

College of Graduate Studies: Theses & Dissertations

Introduction: Athletes have differences in object tracking, search strategies, number and duration of fixations, dynamic visual acuity (DVA), and predictive eye movements than non-athletes (NON). However, these eye functions have not been assessed between athlete groups during a task that encompasses antisaccade and DVA characteristics. Purpose: To evaluate the oculomotor control sport paradigm differences between interceptive (INT) and strategic (STR) Division I collegiate athletes, as well as NON with an antisaccade task (AS) and a sport-like dual task (SDT). Methods: Fifty-seven participants (19 STR, 19 INT, and 19 NON) performed 2 trials of an AS and a SDT. Participants stood …

Polarity And Competition In The Development Of The Calyx Of Held Terminal In The Medial Nucleus Of The Trapezoid Body In The Mouse, Paul Steven Holcomb

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the auditory brainstem, the connection between globular bushy cells of the anteroventral cochlear nucleus and principal cells (PCs) of the medial nucleus of the trapezoid body (MNTB) is created by one of the largest nerve terminals in the central nervous system, the calyx of Held (CH). The characteristics of the CH:MNTB connection—a short developmental period (48-72 hours), accessibility for recording from pre- and postsynaptic components, and clear monoinnervated end point—make this system an ideal model system for studying nervous system development. Model systems undergo stereotyped stages of development, including exuberant overinnervation, competition between terminals, and a refinement of innervation …

Analytical Modeling Of A Communication Channel Based On Subthreshold Stimulation Of Neurobiological Networks, Alireza Khodaei

School of Computing: Dissertations, Theses, and Student Research

The emergence of wearable and implantable machines manufactured artificially or synthesized biologically opens up a new horizon for patient-centered health services such as medical treatment, health monitoring, and rehabilitation with minimized costs and maximized popularity when provided remotely via the Internet. In particular, a swarm of machines at the scale of a single cell down to the nanoscale can be deployed in the body by the non-invasive or minimally invasive operation (e.g., swallowing and injection respectively) to perform various tasks. However, an individual machine is only able to perform basic tasks so it needs to exchange data with the others …

Study Of Self-Similarity In Brain Data, Jennifer Holst

Student Theses

In the area of computer science, past research has found that the concept of self-similarity is present in local and Internet-based network traffic. This study considers the possibility that data traveling through the neuronal network in the human brain is also self-similar. By analyzing publicly available raw EEG data and estimating its Hurst parameter, we find indications that brain data traffic may in fact be self-similar.

Pattern Discovery In Brain Imaging Genetics Via Scca Modeling With A Generic Non-Convex Penalty, Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen, Michael W. Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, Charles D. Smith, Gregory Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose ℓ₁-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the ℓ₁-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce …

Temperature Alters The Amplitude Ratios Of Extracellularly Recorded Action Potentials, Marissa Cruz

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Fleshing Out The Details: Towards A Biologically Realistic Learning Algorithm, Douglas Ryan Schuweiler, Paul A. Garris

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Critical Firing Rate In Synchronous Transitions Of Coupled Neurons, Annabelle Shaffer, Epaminondas Rosa, Rosangela Follmann

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor

Dissertations, Theses, and Capstone Projects

Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.

Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world …