Projects

We aim to understand how neural networks perform critical computations. In sensory systems, a variety of computations extract information from the environment to guide behavior. It is our goal to obtain a comprehensive understanding of how neurons gain specific physiological properties, how they are organized in circuits and how these circuits guide distinct behaviors.

Current projects:

  • The molecular and cellular basis of neural computation
    A central goal of our research is to understand the implementation of neural computations in the visual system. We aim to identify the cellular and molecular mechanisms that shape the physiological properties of neurons, and how these properties enable them to effectively process visual information. We furthermore investigate how neurons are organized in circuits to perform their specific computational tasks. Given that the ultimate goal of visual computations is to achieve appropriate behavioral responses to visual stimuli, we attempt to link both cellular and circuit properties to visually guided behavior.
  • Cell type specific genetic tools for circuit analysis
    The analysis of neural circuits or the molecular machinery of neural computation relies on genetic access to individual cells or cell types, or distinct access to two potential synaptic partners. We have recently developed genetic toolkits to achieve this. We are also continuing to develop tools for a more specific manipulations of subcellular circuit elements.
    Finally, in collaboration with the Schnaitmann and Duch labs, we are exploring ideas to trace and manipulate gap junction – coupled networks.
  • Robust vision in dynamically changing environments
    For sighted animals including ourselves, the visual input is continually changing. Flies exhibit invariant behavioral responses to stimuli of equal contrast across varying luminance (Ketkar and Sporar et al, 2020). We aim to understand where in the visual circuity luminance invariance is achieved. We want to unravel how a post-receptor gain control mechanisms is implemented at the circuit levels, how different cell types become distinct, and how different types of information are ultimately combined to drive behavior. A recent interest of ours also lies in understanding how different visual systems evolved their processing strategies to match their specific behavioral and environmental demands.

 

 

We are furthermore tightly collaborating with the groups of Carlotta Martelli, Christopher Schnaitmann, and Joe Urban, with whom we are sharing lab and office space. You can find more details about their work here:

Silies lab after a lab trip in summer 2021