Sen Song

This paper develops a spatially extended neural network model with spectral theory to show how local connectivity parameters determine global neural dynamics and explains cortical dynamics shifts in mice.

This study examines frequency-specific EEG microstate parameters in relation to anxiety and depression symptoms using a resting-state dataset, finding that alpha band microstates have stronger predictive power than broadband signals.

The paper maps Purkinje cell connectivity to cerebellar nuclear neurons, finding non-random, zone-specific patterns and suggesting that small Purkinje inputs can pause nuclear neuron output.

This study identifies EEG representations of multivariate emotion experience using inter-situation RSA, finding significant correlations in Alpha and Beta bands over frontal and parietal-occipital regions.

The paper identifies opposing transcription signals mediated by CaV1-encoded DCT peptides and shows they are regulated by C-terminus mediated inhibition.

This study introduces RiboRL, a deep reinforcement learning framework for predicting ribosome densities on transcripts, showing improved performance over existing methods.

This study shows that adding recurrent connections to a CNN mimics V1 connectivity patterns and Hebbian rule, suggesting deep learning can model biological neural networks.