Brittany Story

MapperEEG: A topological approach to brain state clustering in EEG recordings

Brittany Story, Zhibin Zhou, Ramesh Srinivasan, Scott E. Kerick et autres

BACKGROUND: Topological data analysis (TDA) has exploded as a tool for analyzing and making sense of high dimensional datasets across a variety of fields. Mapper is a tool from TDA that captures low-dimensional structure from high-dimensional data, precisely the approach needed to …

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EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment

Mikolaj Walczak, Romina Aalishah, Wyatt Mackey, Brittany Story et autres

Deep reinforcement learning agents are often fragile while humans remain adaptive and flexible to varying scenarios. To bridge this gap, we present EDEN, a biologically inspired navigation framework that integrates learned entorhinal-like grid cell representations and reinforcement learning to enable autonomous navigation. …

MapperEEG: A Topological Approach to Brain State Clustering in EEG Recordings

Brittany Story, Zhibin Zhou, Ramesh Srinivasan

Background: Topological data analysis (TDA) has exploded as a tool for analyzing and making sense of high dimensional datasets across a variety of fields. Mapper is a tool from TDA that captures low-dimensional structure from high-dimensional data, precisely the approach needed to …

DialectDecoder: Human/machine teaming for bird song classification and anomaly detection

Brittany Story, Patrick Gillespie, Graham E. Derryberry, Elizabeth P. Derryberry et autres

Classification tasks are some of the most widely known machine learning applications. Computers can classify images, sounds, and patterns with high accuracy, given enough training data, time, and computational resources. After training, computers can perform identification tasks faster than humans and often …

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A topological deep learning framework for neural spike decoding

Edward Clay Mitchell, Brittany Story, David L. Boothe, Piotr J. Franaszczuk et autres

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Additive energy functions have predictable landscape topologies

Brittany Story, Biswajit Sadhu, Henry Adams, Aurora E. Clark

Recent work [Mirth et al., J. Chem. Phys. 154, 114114 (2021)] has demonstrated that sublevelset persistent homology provides a compact representation of the complex features of an energy landscape in 3 N-dimensions. This includes information about all transition paths between local minima …

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Molecular Representations for Machine Learning

Grier M. Jones, Brittany Story, Vasileios Maroulas, Konstantinos D. Vogiatzis

This primer helps the reader understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, you will be able to use various methods to generate machine and/or human …

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Additive energy functions have predictable landscape topologies

Brittany Story, Biswajit Sadhu, Henry Adams, Aurora E. Clark

Recent work (J. Chem. Phys. 154, 114114) has demonstrated that sublevelset persistent homology provides a compact representation of the complex features of an energy landscape in $3N$-dimensions. This includes information about all transition paths between local minima (connected by critical points of …

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A Topological Deep Learning Framework for Neural Spike Decoding

Edward Clay Mitchell, Brittany Story, David L. Boothe, Piotr J. Franaszczuk et autres

The brain's spatial orientation system uses different neuron ensembles to aid in environment-based navigation. Two of the ways brains encode spatial information is through head direction cells and grid cells. Brains use head direction cells to determine orientation whereas grid cells consist …

A Survey of Mathematical Structures for Lunar Networks

Alan Hylton, Robert Short, Jacob Cleveland, Olivia Freides et autres

To sustain the current and increasing accessibility of space, a scalable communications infrastructure (i.e. the Solar System Internet, SSI) is necessary. The goal of this paper is to begin the discovery of the fundamental underlying mathematical structure of space networks to help …

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Support vector machines and Radon's theorem

Henry Adams, Elin Farnell, Brittany Story

A support vector machine (SVM) is an algorithm that finds a hyperplane which optimally separates labeled data points in \begin{document}$ \mathbb{R}^n $\end{document} into positive and negative classes. The data points on the margin of this separating hyperplane are called support vectors. We …

The Impact of Changes in Resolution on the Persistent Homology of Images

Teresa Heiss, Sarah Tymochko, Brittany Story, Adélie Garin et autres

Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requirements for a given sample, but if …

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