Loreen Hertäg

This paper introduces a computational model of the neocortex that uses self-supervised learning to predict sensory inputs, showing layer-specific predictions and matching experimental observations.

This study shows NDNF interneurons in layer 1 modulate inhibition via GABAergic transmission, controlling information flow in pyramidal cells through competition with SOM interneurons.

The paper investigates how cortical circuits enable region-specific credit assignment in reinforcement learning by analyzing the role of VIP and SST interneurons and cholinergic signals.

The paper investigates whether transcriptomic state modulation in GABAergic interneurons generalizes across species by comparing single-cell RNA sequencing data from multiple species.

The paper proposes a network model to estimate sensory and prediction uncertainty using prediction-error circuits and investigates how inhibitory interneurons influence input weighting.

The paper develops a computational model to study how prediction-error neurons form and function in networks with multiple inhibitory interneurons, showing their robustness and functional roles.

The paper develops a computational model of mouse primary visual cortex to show how three interneuron types shape negative prediction-error neurons through experience-dependent inhibitory plasticity.

The paper proposes a computational model showing how three interneuron types interact to redistribute somato-dendritic inhibition in pyramidal cells.