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Found 14 papers, 7 papers with code
Generalizable, real-time neural decoding with hybrid state-space models
We evaluate POSSM's decoding performance and inference speed on intracortical decoding of monkey motor tasks, and show that it extends to clinical applications, namely handwriting and speech decoding in human subjects.
Neural Encoding and Decoding at Scale
Altogether, our approach is a step towards a foundation model of the brain that enables seamless translation between neural activity and behavior.
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution
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 in its neural activity remains out of reach.
GraphFM: A Scalable Framework for Multi-Graph Pretraining
We demonstrate the efficacy of our approach by training a model on 152 different graph datasets comprising over 7. 4 million nodes and 189 million edges, establishing the first set of scaling laws for multi-graph pretraining on datasets spanning many domains (e. g., molecules, citation and product graphs).
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-structured data.
Ranked #1 on
Node Classification
on AMZ Comp
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future.
Learning signatures of decision making from many individuals playing the same game
In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
Learning Behavior Representations Through Multi-Timescale Bootstrapping
Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample.
Large-Scale Representation Learning on Graphs via Bootstrapping
To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.
Making transport more robust and interpretable by moving data through a small number of anchor points
Optimal transport (OT) is a widely used technique for distribution alignment, with applications throughout the machine learning, graphics, and vision communities.
