Ari S. Benjamin

TissueFormer predicts population-level phenotypes from single-cell data, outperforming existing methods in predicting COVID-19 severity and cortical area identity.

This paper introduces a bidirectional recurrent model of attention inspired by biological mechanisms and neural networks, capable of replicating behavioral and neural findings related to visual attention.

The paper shows that gradient descent learning in neural networks creates representations sensitive to common image features, mirroring efficient coding principles observed in animal sensory systems.

This study compares GLMs to machine learning methods for predicting neural spikes and finds that modern methods like XGBoost and ensembles are more accurate and robust.