Sanjukta Krishnagopal
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View article: A brain-wide map of neural activity during complex behaviour
A brain-wide map of neural activity during complex behaviour Open
A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with previous expectations to initiate movements and make decisions 1 . It is difficult to meet this challeng…
View article: Fractal dimensions of complex networks: advocating for a topological approach
Fractal dimensions of complex networks: advocating for a topological approach Open
Topological Data Analysis (TDA) uses insights from topology to create representations of data able to capture global and local geometric and topological properties. Its methods have successfully been used to develop estimations of fractal …
View article: Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies
Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies Open
Spatio-temporal forecasting is critical in applications such as traffic prediction, energy demand modeling, and weather monitoring. While Graph Attention Networks (GATs) are popular for modeling spatial dependencies, they rely on predefine…
View article: A Network-Based Measure of Cosponsorship Influence on Bill Passing in the United States House of Representatives
A Network-Based Measure of Cosponsorship Influence on Bill Passing in the United States House of Representatives Open
Each year, the United States Congress considers thousands of legislative proposals to select bills to present to the US President to sign into law. Naturally, the decision processes of members of Congress are subject to peer influence. In …
View article: Bounded-Confidence Models of Opinion Dynamics with Neighborhood Effects
Bounded-Confidence Models of Opinion Dynamics with Neighborhood Effects Open
We generalize bounded-confidence models (BCMs) of opinion dynamics by incorporating neighborhood effects. In a BCM, interacting agents influence each other through dyadic influence if their opinions are sufficiently similar to each other. …
View article: Topology and dynamics of higher-order multiplex networks
Topology and dynamics of higher-order multiplex networks Open
Higher-order networks are gaining significant scientific attention due to their ability to encode the many-body interactions present in complex systems. However, higher-order networks have the limitation that they only capture many-body in…
View article: Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups
Hate speech and hate crimes: a data-driven study of evolving discourse around marginalized groups Open
This study explores the dynamic relationship between online discourse, as observed in tweets, and physical hate crimes, focusing on marginalized groups. Leveraging natural language processing techniques, including keyword extraction and to…
View article: Topology and dynamics of higher-order multiplex networks
Topology and dynamics of higher-order multiplex networks Open
Higher-order networks are gaining significant scientific attention due to their ability to encode the many-body interactions present in complex systems. However, higher-order networks have the limitation that they only capture many-body in…
View article: A modular architecture for organizing, processing and sharing neurophysiology data
A modular architecture for organizing, processing and sharing neurophysiology data Open
View article: Graph Neural Tangent Kernel: Convergence on Large Graphs
Graph Neural Tangent Kernel: Convergence on Large Graphs Open
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can be hard to train on large-graph data, where their learning dynamics are not well understood. We investigate the training dynamics of large-…
View article: The collective vs individual nature of mountaineering: a network and simplicial approach
The collective vs individual nature of mountaineering: a network and simplicial approach Open
View article: Stroke recovery phenotyping through network trajectory approaches and graph neural networks
Stroke recovery phenotyping through network trajectory approaches and graph neural networks Open
Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of …
View article: The collective vs individual nature of mountaineering: a network and simplicial approach
The collective vs individual nature of mountaineering: a network and simplicial approach Open
Mountaineering is a sport of contrary forces: teamwork plays a large role in mental fortitude and skills, but the actual act of climbing, and indeed survival, is largely individualistic. This work studies the effects of the structure and t…
View article: Success at High Peaks: A Multiscale Approach Combining Individual and Expedition-Wide Factors
Success at High Peaks: A Multiscale Approach Combining Individual and Expedition-Wide Factors Open
View article: Encoding priors in the brain: a reinforcement learning model for mouse decision making
Encoding priors in the brain: a reinforcement learning model for mouse decision making Open
In two-alternative forced choice tasks, prior knowledge can improve performance, especially when operating near the psychophysical threshold. For instance, if subjects know that one choice is much more likely than the other, they can make …
View article: Stroke recovery phenotyping through network trajectory approaches and graph neural networks
Stroke recovery phenotyping through network trajectory approaches and graph neural networks Open
Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of …
View article: Stroke recovery phenotyping through network trajectory approaches and graph neural networks
Stroke recovery phenotyping through network trajectory approaches and graph neural networks Open
Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of …
View article: Success at high peaks: a multiscale approach combining individual and\n expedition-wide factors
Success at high peaks: a multiscale approach combining individual and\n expedition-wide factors Open
This work presents a network-based data-driven study of the combination of\nfactors that contribute to success in mountaineering. It simultaneously\nexamines the effects of individual factors such as age, gender, experience\netc., as well …
View article: A rapid and efficient learning rule for biological neural circuits
A rapid and efficient learning rule for biological neural circuits Open
The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to…
View article: Encoded Prior Sliced Wasserstein AutoEncoder for learning latent\n manifold representations
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent\n manifold representations Open
While variational autoencoders have been successful in several tasks, the use\nof conventional priors are limited in their ability to encode the underlying\nstructure of input data. We introduce an Encoded Prior Sliced Wasserstein\nAutoEnc…
View article: Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations Open
While variational autoencoders have been successful in several tasks, the use of conventional priors are limited in their ability to encode the underlying structure of input data. We introduce an Encoded Prior Sliced Wasserstein AutoEncode…
View article: Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping
Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping Open
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for e…
View article: Identifying and predicting Parkinson’s disease subtypes through trajectory clustering via bipartite networks
Identifying and predicting Parkinson’s disease subtypes through trajectory clustering via bipartite networks Open
Chronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes a…
View article: Multi-layer Trajectory Clustering: A Network Algorithm for Disease\n Subtyping
Multi-layer Trajectory Clustering: A Network Algorithm for Disease\n Subtyping Open
Many diseases display heterogeneity in clinical features and their\nprogression, indicative of the existence of disease subtypes. Extracting\npatterns of disease variable progression for subtypes has tremendous\napplication in medicine, fo…
View article: Trajectory Clustering in Multi-layer Networks: Identifying Parkinson's Subtypes.
Trajectory Clustering in Multi-layer Networks: Identifying Parkinson's Subtypes. Open
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for e…
View article: UNCOVERING PATTERNS IN COMPLEX DATA WITH RESERVOIR COMPUTING AND NETWORK ANALYTICS: A DYNAMICAL SYSTEMS APPROACH
UNCOVERING PATTERNS IN COMPLEX DATA WITH RESERVOIR COMPUTING AND NETWORK ANALYTICS: A DYNAMICAL SYSTEMS APPROACH Open
In this thesis, we explore methods of uncovering underlying patterns in complex data, and making predictions, through machine learning and network science. With the availability of more data, machine learning for data analysis has advanced…
View article: Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach
Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach Open
We investigate the ways in which a machine learning architecture known as Reservoir Computing learns concepts such as “similar” and “different” and other relationships between image pairs and generalizes these concepts to previously unseen…