Eva L. Dyer
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View article: Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning
Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning Open
In domains such as healthcare, finance, and e-commerce, the temporal dynamics of relational data emerge from complex interactions-such as those between patients and providers, or users and products across diverse categories. To be broadly …
View article: Author response: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Author response: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
View article: Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data
Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Open
Transformer models have become state-of-the-art in decoding stimuli and behavior from neural activity, significantly advancing neuroscience research. Yet greater transparency in their decision-making processes would substantially enhance t…
View article: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical l…
View article: Neural Encoding and Decoding at Scale.
Neural Encoding and Decoding at Scale. Open
Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting…
View article: Author response: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Author response: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
View article: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical l…
View article: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical l…
View article: Author response: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
Author response: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
View article: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical l…
View article: In vivo cell-type and brain region classification via multimodal contrastive learning
In vivo cell-type and brain region classification via multimodal contrastive learning Open
Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalab…
View article: GraphFM: A Scalable Framework for Multi-Graph Pretraining
GraphFM: A Scalable Framework for Multi-Graph Pretraining Open
Graph neural networks are typically trained on individual datasets, often requiring highly specialized models and extensive hyperparameter tuning. This dataset-specific approach arises because each graph dataset often has unique node featu…
View article: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains Open
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical l…
View article: Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance Open
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is widely studied in cases of datasets with sample imbalance, but …
View article: Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution
Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution Open
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded…
View article: Why the simplest explanation isn’t always the best
Why the simplest explanation isn’t always the best Open
Respiratory virus infections in humans are a significant global health concern, causing a wide range of diseases with substantial morbidity and mortality worldwide. This underscores the urgent need for effective interventions to reduce the…
View article: A Unified, Scalable Framework for Neural Population Decoding
A Unified, Scalable Framework for Neural Population Decoding Open
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challen…
View article: A Unified, Scalable Framework for Neural Population Decoding.
A Unified, Scalable Framework for Neural Population Decoding. Open
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challen…
View article: Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes
Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes Open
View article: Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons
Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons Open
The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function cent…
View article: LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration Open
Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge, we propose a novel approach for distribution-aware latent …
View article: Half-Hop: A graph upsampling approach for slowing down message passing
Half-Hop: A graph upsampling approach for slowing down message passing Open
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this wo…
View article: Half-Hop: A graph upsampling approach for slowing down message passing.
Half-Hop: A graph upsampling approach for slowing down message passing. Open
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this wo…
View article: A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior
A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior Open
Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.…
View article: De novo evolution of macroscopic multicellularity
De novo evolution of macroscopic multicellularity Open
View article: Learning signatures of decision making from many individuals playing the same game
Learning signatures of decision making from many individuals playing the same game Open
Human behavior is incredibly complex and the factors that drive decision making--from instinct, to strategy, to biases between individuals--often vary over multiple timescales. In this paper, we design a predictive framework that learns re…
View article: Detecting change points in neural population activity with contrastive metric learning
Detecting change points in neural population activity with contrastive metric learning Open
Finding points in time where the distribution of neural responses changes (change points) is an important step in many neural data analysis pipelines. However, in complex and free behaviors, where we see different types of shifts occurring…
View article: Transcriptomic cell type structures in vivo neuronal activity across multiple timescales
Transcriptomic cell type structures in vivo neuronal activity across multiple timescales Open
View article: Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis Open
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future. While some success has been found in building representations of behavior under constrained or simplifi…
View article: An active learning framework for personalized deep brain stimulation
An active learning framework for personalized deep brain stimulation Open
Background: To personalize deep brain stimulation (DBS), we need to identify a link between DBS parameters and the neural response of an individual. The existing approach based on random or empirical sampling (RS) is time-consuming, costly…