Maria C. Dadarlat

This study shows that using a redundant encoding algorithm for intracortical microstimulation improves learning and multisensory integration in navigation tasks.

The paper identifies a shared population code in mouse visual cortex that encodes sensory reliability through coordinated changes in response gain, selectivity, and variability across areas.

The paper shows that inhibitory neurons shape circuit changes after microstimulation in the awake mouse cortex by tracking neural activity before, during, and after stimulation.

The paper develops a mouse behavioral task to study integration of visual and artificial proprioceptive feedback using intracortical microstimulation, showing improved performance with multimodal feedback.

The paper analyzes neural activity in mouse sensorimotor cortex during passive limb movements to show that population-level encoding of somatosensory information is structured for sensorimotor integration.

This study uses a deep learning model to decode multi-limb movements from low temporal resolution calcium imaging data in mice.

This study investigates how electrical stimulation affects inhibitory and excitatory neurons in the mammalian cortex, finding that increasing stimulation amplitude shifts the balance toward inhibition and correlates with behavioral changes.

The paper uses flow stimuli to show that mouse V1 neurons respond to higher spatial frequencies than previously thought, challenging conventional linear models of visual processing.

This study shows locomotion enhances visual information encoding in mouse V1 by increasing firing rates and reducing noise correlations, improving stimulus discriminability.