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arxiv.org
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<strong>PRISM: A Hierarchical Multiscale Approach for Time Series Forecasting</strong>
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biorxiv.org
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<strong>Projection-specific Routing of Odor Information in the Olfactory Cortex</strong>
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arxiv.org
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<strong>Narrative of time across scales (NOTS)</strong>
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arxiv.org
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<strong>GraphFM: A generalist graph transformer that learns transferable representations across diverse domains</strong>
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neurips.cc
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Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
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openreview.net
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RGP: A Cross-Attention based Graph Transformer for Relational Deep Learning
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neurips.cc
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Exploiting All Laplacian Eigenvectors for Node Classification with Graph Transformers
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neurips.cc
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RELATE: A Schema-Agnostic Cross-Attention Encoder for Multimodal Relational Graphs
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neurips.cc
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A scalable self-supervised method for modeling human intracranial recordings during natural behavior
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arxiv.org
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Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting
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arxiv.org
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<strong>Generalizable, real-time neural decoding with hybrid state-space models.</strong>
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arxiv.org
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Learning Sinkhorn divergences for supervised change point detection
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arxiv.org
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Neural Encoding and Decoding at Scale
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openreview.net
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<strong>Time Series Domain Adaptation via Channel-Selective Representation Alignment</strong>
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openreview.net
|
Multi-session, multi-task neural decoding from distinct cell-types and brain regions
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biorxiv.org
|
In vivo cell-type and brain region classification via multimodal contrastive learning
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|
arxiv.org
|
<strong>Your contrastive learning problem is secretly a distribution alignment problem</strong>
|
|
arxiv.org
|
<strong>Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution</strong>,
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|
arxiv.org
|
<strong>The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions</strong>
|
|
elifesciences.org
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A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains
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|
arxiv.org
|
<strong>Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance</strong>
|
|
biorxiv.org
|
<strong>Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons</strong>
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nature.com
|
<strong>A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior</strong>
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openreview.net
|
<strong>GAFormer: Enhancing time-series transformers through group-aware embeddings</strong>
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|
arxiv.org
|
<strong>The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective</strong>
|
|
arxiv.org
|
<strong>LatentDR: Improving model generalization through sample-aware latent degradation and restoration</strong>
|
|
pnas.org
|
<strong>Why the simplest explanation isn't always the best</strong>
|
|
arxiv.org
|
<strong>A unified, scalable framework for neural population decoding</strong>
|
|
poyo-brain.github.io
|
<strong>Page</strong>
|
|
arxiv.org
|
<strong>Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis</strong>
|
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multiscale-behavior.github.io
|
<strong>Web</strong>
|
|
sciencedirect.com
|
<strong>Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes</strong>
|
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proceedings.mlr.press
|
<strong>Half-Hop: A graph upsampling approach for slowing down message passing</strong>
|
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cell.com
|
<strong>Transcriptomic cell type structures in vivo neuronal activity across multiple timescales</strong>
|
|
github.com
|
<strong>Code</strong>
|
|
nature.com
|
<strong>De novo evolution of macroscopic multicellularity</strong>
|
|
ieeexplore.ieee.org
|
<strong>Learning signatures of decision making from many individuals playing the same game</strong>
|
|
sites.gatech.edu
|
<strong>Detecting change points in neural population activity with contrastive metric learning</strong>
|
|
arxiv.org
|
<strong>Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers</strong>
|
|
arxiv.org
|
<strong>MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction</strong>
|
|
mtneuro.github.io
|
<strong>Page</strong>
|
|
arxiv.org
|
<strong>Learning Behavior Representations Through Multi-Timescale Bootstrapping</strong>
|
|
sites.gatech.edu
|
<strong>Building representations of different brain areas through hierarchical point cloud networks</strong>
|
|
arxiv.org
|
<strong>Large-Scale Representation Learning on Graphs via Bootstrapping</strong>
|
|
github.com
|
<strong>Code</strong>
|
|
nature.com
|
<strong>Aligning latent representations of neural activity</strong>
|
|
arxiv.org
|
<strong>Learning Sinkhorn divergences for supervised change point detection</strong>
|
|
biorxiv.org
|
Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden
|
|
arxiv.org
|
<strong>Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity</strong>
|
|
arxiv.org
|
<strong>Mine Your Own vieW: Self-supervised learning through across-sample prediction</strong>
|
|
sslneurips21.github.io
|
<strong>Using self-supervision and augmentations to build insights into neural coding</strong>
|
|
datasets-benchmarks-proceedings.neurips.cc
|
<strong>Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity</strong>
|
|
neurallatents.github.io
|
<strong>Page</strong>
|
|
sites.gatech.edu
|
<strong>Multi-scale modeling of neural structure in X-ray imagery</strong>
|
|
proceedings.mlr.press
|
<strong>Making transport more robust and interpretable by moving data through a small number of anchor points</strong>
|
|
sites.gatech.edu
|
Poster
|
|
proceedings.mlr.press
|
<strong>Bayesian optimization for modular black-box systems with switching costs</strong>
|
|
doi.org
|
<strong>Toward a Reproducible, Scalable, Framework for Processing Large Neuroimaging Datasets</strong>
|
|
doi.org
|
<strong>A generative modeling approach for interpreting population-level variability in brain structure</strong>
|
|
joss.theoj.org
|
<strong>Pyglmnet: Python implementation of elastic-net regularized generalized linear models</strong>
|
|
github.com
|
<strong>Code</strong>
|
|
doi.org
|
<strong>Multi-sensory integration in the mouse cortical connectome using a network diffusion model</strong>
|
|
nature.com
|
<strong>A three-dimensional thalamocortical dataset for characterizing brain heterogeneity</strong>
|
|
bossdb.org
|
<strong>Interactive Atlas</strong>
|
|
sites.gatech.edu
|
<strong>A deep feature learning approach for mapping the brain’s microarchitecture and organization</strong>
|
|
biorxiv.org
|
<strong>Extended version</strong>
|
|
sites.gatech.edu
|
<strong>Modeling variability in brain architecture with deep feature learning</strong>
|
|
sciencedirect.com
|
<strong>Brain Mapping at High Resolutions: Challenges and Opportunities</strong>
|
|
sites.gatech.edu
|
<strong>Hierarchical Optimal Transport for Multimodal Distribution Alignment</strong>
|
|
github.com
|
Code
|
|
sites.gatech.edu
|
<strong>Generative models and abstractions for large-scale neuroanatomy datasets</strong>
|
|
doi.org
|
<strong>Latent factors and dynamics in motor cortex and their application to brain-machine interfaces</strong>
|
|
journals.plos.org
|
<strong>Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation</strong>
|
|
dropbox.com
|
<strong>Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data</strong>
|
|
doi.org
|
<strong>Enhancing low-dose X-ray tomography through a deep convolutional neural network</strong>
|
|
doi.org
|
<strong>A cryptography-based approach to movement decoding</strong>
|
|
kordinglab.com
|
Code
|
|
doi.org
|
<strong>Quantifying mesoscale neuroanatomy using X-ray microtomography</strong>
|
|
github.com
|
Code
|
|
github.com
|
Data
|
|
doi.org
|
<strong>RankMap: A platform-aware framework for distributed learning from dense datasets</strong>
|
|
github.com
|
<span style="color: #993366;">Code</span>
|
|
arxiv.org
|
<strong>Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes</strong>
|
|
dl.dropboxusercontent.com
|
Poster
|
|
dl.dropboxusercontent.com
|
<span style="color: #993366;">Slides</span>
|
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jmlr.org
|
<strong>Greedy feature selection for subspace clustering</strong>
|
|
dx.doi.org
|
<strong>A robust and efficient method to recover neural events from noisy and corrupted data</strong>
|
|
github.com
|
<span style="color: #993366;">Code</span>
|
WordPress