KLI Colloquia are informal, public talks that are followed by extensive dissussions. Speakers are KLI fellows or visiting researchers who are interested in presenting their work to an interdisciplinary audience and discussing it in a wider research context. We offer three types of talks:
1. Current Research Talks. KLI fellows or visiting researchers present and discuss their most recent research with the KLI fellows and the Vienna scientific community.
2. Future Research Talks. Visiting researchers present and discuss future projects and ideas togehter with the KLI fellows and the Vienna scientific community.
3. Professional Developmental Talks. Experts about research grants and applications at the Austrian and European levels present career opportunities and strategies to late-PhD and post-doctoral researchers.
- The presentation language is English.
- If you are interested in presenting your current or future work at the KLI, please contact the Scientific Director or the Executive Manager.
In this talk, I will briefly introduce the framework of information theory as applied to biological signaling networks. Known under the name of “efficient coding”, this framework has been able to quantitatively explain various (nontrivial) properties of neural processing from first principles. In this regard, applications of efficient coding represent true “ab initio” predictions, rather than fits of specific mathematical models to data. I will then present our attempts to build a similarly predictive theory for genetic regulatory networks, along with a specific application to the gap gene network in the fruit fly. I will conclude with a few thoughts on why information transmission through signaling networks might be implicitly selected for during evolutionary adaptation.
Gasper Tkacik joined IST Austria in 2011 as an Assistant Professor. Previously, he was a postdoc with Vijay Balasubramanian and Phil Nelson at University of Pennsylvania, working on the theory of neural coding and specifically exploring population coding and adaptation in the retina. He finished his PhD in Physics at Princeton with Bill Bialek and Curt Callan in 2007, studying how biological networks can reliably transmit and process information in the presence of intrinsic noise and corrupted signals. He is broadly interested in uncovering general principles that underlie efficient biological computation. He works both on data-driven and purely theoretical problems, and combines approaches from statistical physics, information theory, and machine learning.