A spatially-tuned normalization model accounts for neuronal responses to attended or unattended stimuli that are presented inside the classical receptive field or the surround, and explains various other observations.
A comprehensive, data-driven and interpretable nonlinear computational modeling framework based on deep neural networks uncovers different nonlinear transformations of speech signal in the human auditory cortex.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.
Sequential introduction of transcription factors enables large-scale generation of induced motor neurons (iMNs) from human somatic cells, and transplantation of iMNs exhibit therapeutic effects in spinal cord injury model.
The conductance-based encoding model creates a new bridge between statistical models and biophysical models of neurons, and infers visually-evoked excitatory and inhibitory synaptic conductances from spike trains in macaque retina.
The BB model explains spatial cognition in terms of interactions between specific neuronal populations, providing a common computational framework for the human neuropsychological and in vivo animal electrophysiological literatures.