5 papers
Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining
Sangyoon Bae, Mehdi Azabou, Blake Richards +1
Neural recordings exhibit a distinctive form of heterogeneity rooted in differences in cell types, intrinsic circuit dynamics, and stochastic stimulus-response variability that goe…
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…
Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Vinam Arora, Divyansha Lachi, Ian J. Knight +5
Neurons process information in ways that depend on their cell type, connectivity, and the brain region in which they are embedded. However, inferring these factors from neural acti…
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…
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…