22 citations · 28 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh +7
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…
Learning signatures of decision making from many individuals playing the same game
Michael J Mendelson, Mehdi Azabou, Suma Jacob +5
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…