4 papers
GraphFM: A generalist graph transformer that learns transferable representations across diverse domains
Divyansha Lachi, Mehdi Azabou, Vinam Arora +1
Graph neural networks (GNNs) are often trained on individual datasets, requiring specialized models and significant hyperparameter tuning due to the unique structures and features…
Generalizable, real-time neural decoding with hybrid state-space models
Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao +4
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subje…
A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
Georgios Mentzelopoulos, Ioannis Asmanis, Konrad P. Kording +3
Brain-computer interfaces (BCIs) promise to enable vital functions, such as speech and prosthetic control, for individuals with neuromotor impairments. Central to their success are…
Neural Encoding and Decoding at Scale
Yizi Zhang, Yanchen Wang, Mehdi Azabou +7
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…