{PR[AI]RIE} Colloquium - Machine learning with mechanistic models Past

In this talk, I will describe two projects using machine learning methods to build and optimize simulations of mechanistic models from neuroscience and optical physics. Such mechanistic models have a one to one correspondence with the world, enabling clear interpretability, but they can be challenging to optimize. In contrast, blackbox models constructed from modern deep networks are designed for ease of optimization but lack interpretability. In our work, we combine deep networks with mechanistic models to achieve the best of both worlds.

The first project will describe the development of a programmable microscope, a new kind of software microscope with millions of free parameters which can enable new forms of imaging, but which requires in silico optimization of its parameters. The second project will describe a connectome constrained simulation of the fruit fly visual system, in which each neuron corresponds to a real neuron on the fly brain and each connection corresponds to a real connection in the brain. This new kind of mechanistic model of the nervous system uses only measurements of neural connectivity measured in a dead brain to predict neural activity in the living brain. 

via Zoom: https://u-paris.zoom.us/j/82231267433?pwd=SHl6YkpIM3ZFck5oNTN4UWR1dkRldz09