Our goal is to understand the fundamental principles for the function and organization of neural circuits involved in estimating an animal’s own movement, especially in the context of visually guided locomotion. Many basic functions of our brain, from motor control, to more cognitive operations such as navigation, critically depend on self-movement estimation. We investigate which circuits are involved in this representation, and what computations these circuits perform. In addition, we aim to identify the activity dynamics and mechanisms by which these computations are generated.
We employ multiple methods to record and perturb neural activity, to analyze the structure of networks, to quantify different aspects of behavior, and to model functional circuits
Our strategy focuses on connecting neural activity dynamics to the locomotive behavior of the fruitfly, Drosophila melanogaster. We employ multiple methods to record and reversibly perturb neural activity in behaving flies, to analyze the structure of interconnected neurons, to quantify different aspects of the fly’s locomotive behavior, and to model functional networks. This multidisciplinary approach, together with the ever-expanding genetic toolkit of the fruitfly, allows us to find mechanistic explanations for how multi-sensory and sensorimotor integration processes in the brain are used to guide adaptive behavior.
Sensory processing and motor actions are intimately linked in many aspects of brain function. Examples include active sensing, goal-directed locomotion and motor learning. We use these behavioral contexts to investigate the underlying operational principles of sensorimotor processing.
We work with “freely moving” and “tethered” behavioral paradigms in virtual reality-like worlds designed to probe how the fly uses her own movements together with visual feedback to guide her walking movements
Identification of circuits
To identify neural elements we apply a plethora of tools, including behavioral, physiological and anatomical methods. We then map connectivity among candidate neurons by combining chemical, optical and electrical techniques.
Probing neural activity
We apply quantitative analytical tools to correlate patterns of neural activity with behavior, and make predictions about the contribution of distinct groups of neurons to different aspects of behavior. These predictions are tested with precise manipulations of neural activity and/or of sensory stimuli.
Understanding neural dynamics
We construct biological inspired models to test and understand how a network of interconnected neurons generates the activity dynamics observed in the context of locomotive behavior.
The computational principles that govern motor-sensory coordination
Electrophysiology, Optical tools, Behavior, Virtual reality, Whiteboard and literature
Models and Regions
Drosophila melanogaster, Sensory, Premotor and Motor brain areas