Circuits and network dynamics in synthetic biology and neuroscience

1 - Neuroscience Network

Data in neuroscience has long been limited to the recording of a few units (neurons) in anesthetized animals. Recent technological developments now make it possible to monitor and even manipulate the dynamics of extended neuronal networks in behaving animals. These new data can thus be confronted with theoretical or numerical network models inspired by statistical physics. 

In this course, we will delve into both facets of modern integrative neuroscience, encompassing both theoretical and experimental approaches. The lectures will revolve around recent research papers and will be complemented by a Python project centered on working with spiking neural networks (SNNs).

Outline :

• Introduction to neuroscience
• New experimental approaches for the recording of extended neural networks.
• Python project: Training of a Spiking Neural Network (SSN) model to solve a sound localization task 
• Persistence in neural circuits and attractor models.
• Data-driven network models in neuroscience.

2 - Synthetic Biology Circuits

Recent technological development in molecular biology have revolutionized our ability to design, build and manipulate genetic networks in cells. This led to the development of Synthetic Biology which links biology to engineering and physics to program advanced cellular functions. Here we will present the basics of synthetic biology, discuss its limitations and give theoretical and experimental examples on how control theory can be applied to synthetic biology to build safer and more robust genetic networks.

Outline :

• Synthetic biology 101.
• Applications and limitations of synthetic biology.
• Control theory and Cybergenetics.