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Computational Neuroscience Commons

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Full-Text Articles in Computational Neuroscience

Timing Is Everything: Temporal Dynamics Of Brain Activity Using The Human Connectome Project, Francesca Lofaro Jan 2019

Timing Is Everything: Temporal Dynamics Of Brain Activity Using The Human Connectome Project, Francesca Lofaro

Summer Research

Most neuroimaging studies produce snapshots of brain activity. The goal of this project is to examine the temporal dynamics of how these areas interact through time, using fear as a case study to assess how regions involved in fear interact. Working with Matlab computer code, I sort through the large fMRI dataset known as the Human Connectome Project to extract neuroimaging data from patients with different NIH Toolbox Fear-Somatic survey scores to assess the temporal dynamics between brain regions. The results will allow an understanding beyond which areas are involved, and instead will provide a picture of how these areas …


Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang Feb 2016

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …


Design, Programming, And User-Experience, Kaila G. Manca May 2015

Design, Programming, And User-Experience, Kaila G. Manca

Honors Scholar Theses

This thesis is a culmination of my individualized major in Human-Computer Interaction. As such, it showcases my knowledge of design, computer engineering, user-experience research, and puts into practice my background in psychology, com- munications, and neuroscience.

I provided full-service design and development for a web application to be used by the Digital Media and Design Department and their students.This process involved several iterations of user-experience research, testing, concepting, branding and strategy, ideation, and design. It lead to two products.

The first product is full-scale development and optimization of the web appli- cation.The web application adheres to best practices. It was …


Derivation Of A Novel Efficient Supervised Learning Algorithm From Cortical-Subcortical Loops, Ashok Chandrashekar, Richard Granger Jan 2012

Derivation Of A Novel Efficient Supervised Learning Algorithm From Cortical-Subcortical Loops, Ashok Chandrashekar, Richard Granger

Dartmouth Scholarship

Although brain circuits presumably carry out powerful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications. We forward a novel analysis of a subset of functions of cortical-subcortical loops, which constitute more than 80% of the human brain, thus likely underlying a broad range of cognitive functions. We describe a family of operations performed by the derived method, including a non-standard method for supervised classification, which may underlie some forms of cortically dependent associative learning. The novel supervised classifier is compared against widely used algorithms for …