Jim Berg

The paper identifies transcriptomic and morpho-electric diversity in human neocortical layer 1 interneurons, revealing greater subclass distinction compared to mouse.

The paper uses viral labeling and Patch-seq to define morpho-electric properties of GABAergic interneurons in human neocortex, revealing functional insights and reclassifying some SST neurons into PVALB subclass.

The paper investigates target cell-specific synaptic dynamics between excitatory and inhibitory neurons in human neocortex, finding similarities to rodent studies.

The paper predicts MET-type identities from EM data and shows they have distinct connectivity patterns and synapse targets.

This study characterizes and simulates changes in human hippocampal granule cells during epilepsy progression, identifying ion channel modifications that increase excitability.

The paper generates and evaluates over 130 million single-neuron models with active conductances to link electrophysiology, morphology, and transcriptomics across cortical cell types.

The paper describes an optimized Patch-seq technique for high-quality electrophysiological, morphological, and transcriptomic characterization of neurons, along with software tools for data acquisition and analysis.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper identifies transcriptomic diversity in human supragranular neurons and links it to functional specialization and disease vulnerability.

The paper classifies neurons in mouse visual cortex into 17 electrophysiological and 35 morphological types using unsupervised methods and links them to transcriptomic types.

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.

The paper develops generalized leaky integrate-and-fire models to classify neuron types in the mammalian neocortex using electrophysiological data.