Lisa Fenk

Lisa Fenk serves as a Lise Meitner Research Group Leader at the Max Planck Institute for Biological Intelligence in Martinsried, Germany, where she leads the Active Sensing research group. Her position represents a prestigious independent research leadership role within the Max Planck Society's framework for early-career scientists.

Dr. Fenk's research focuses on the neural mechanisms of active sensing, particularly investigating how visual systems differentiate between self-generated and external sensory inputs. Her groundbreaking work revealed that fruit flies move their retinas via tiny muscles in ways surprisingly similar to vertebrate eye movements. She leverages Drosophila's advanced genetic, physiological, and behavioral methods to understand how small brains form predictions that inform perception and behavior. Her research spans visual neuroscience, sensory processing, and comparative neuroethology with particular expertise in eye movement systems across species.

Analysis of Dr. Fenk's publication record reveals a strong trajectory in visual neuroscience with emphasis on active sensing mechanisms. Her work demonstrates consistent innovation in understanding how organisms extract relevant visual features through purposeful eye movements. The research spans multiple model systems including Drosophila, spiders, and comparative analyses of visual processing across species, with increasing focus on neural circuit mechanisms in recent years.

Dr. Fenk maintains an active research program with recent publications in high-impact journals including Science (2024), Nature (2022), and Current Biology (2021). Her work demonstrates strong international collaboration across neuroscience institutions in Europe and North America.

As a Lise Meitner Group Leader, Dr. Fenk directs her own research laboratory investigating the fundamental mechanisms of active vision. Her team employs genetic, physiological, and behavioral approaches in Drosophila to uncover how brains differentiate between sensory inputs generated by self-movement versus external events, with broader implications for understanding predictive processing in neural systems.