Our mission is to understand the mechanisms and functions of neural computations through the lens of spatial navigation.

We use the brain of the fruit fly Drosophila to investigate these questions. The genetic toolbox of this organism allows us to rapidly generate new reagents to label or manipulate specific classes of neurons in the brain. Many individual neurons are uniquely identifiable across different brains, and they have fairly stereotyped synaptic inputs and outputs. This allows us to build up a cumulative picture of each neuron in a network. Crucially, it allows us to understand a neuron's activity patterns in light of its synaptic connectivity patterns.

Because many neural systems in various species face the same constraints, we believe that some of the lessons we learn from this simple brain will provide clues to understanding similar problems in more complex brains.

Key questions:

* How do neurons extract and route pieces of information that are relevant to motor control tasks on different spatial scales and temporal scales?

* How are signals from different sensory systems merged into a common coordinate frame?

* How are past experiences integrated with immediate sensory feedback?

* How are external sensory cues integrated with self-motion signals and internal goals?

* How do spatial maps in the brain interface with motor control systems to control locomotion paths?

* How do neural networks learn about new environments "on the fly", during active exploration?

* What biophysical mechanisms implement these neural computations, at the level of ion channels, synapses, cells, and network connectivity patterns?

* How do these particular neural computations (and their implementation) help us understand the behaviors that engage these networks, as well as the constraints that shaped these networks and behaviors?

Techniques: Each study typically combines several of the following approaches:

* in vivo electrophysiological recording of neural activity

* in vivo optical imaging of neural activity

* semi-automated reconstruction of neural networks at single-synapse resolution from a whole-brain electron microscopy dataset

* genetic manipulation of specific cell classes

* behavioral measurements, often with simultaneous imaging or electrophysiology

* computational modeling