Bio-inspired neural networks for navigation
The projects goal is to understand the structure and dynamics of neural network models trained on navigational task and compare with experimental studies.
Fig. 1: Grid cells observed in mouse and in a recurrent neural network, both trained on a navigation task.
Aritificlal Intelligence has impact across science and society, but the basic mechanisms and structures are still poorly understood. For example, it has recently been demonstrated that when artificial agents controlled by recurrent neural networks are trained to navigate, they spontaneously form structures that are similar to the grid cell structures found in the brain. Grid cells were the basis for the 2014 Nobel.
In this project, you will implement and study the dynamics and structures of recurrent neural networks that are trained either with supervised or reinforcement learning to navigate. You will learn to apply tools from both physics, neuroscience and artificial intelligence to understand the formation of grid cells and other structures, and how these representations impact navigation and can be reused on e.g. temporal recognition tasks.
These studies will be done in close collaboration with our leading experimental neuroscience lab which provides you with unique access to experimental studies of similar processes. We believe this close coupling between experiment and modeling and between physics, computer science and bioscience will be essential for next breakthroughs in our understanding of both artificial and biological neural networks.
The project include collaborations with leading groups at Harvard University and the University of California San Diego.
- You must have a master degree in physics, computational neuroscience, mathematics, mechatronics/control theory/cybernetics, or artificial intelligence.
- Documented experience from computational modeling in statistical physics, scientific programming or implementation and studies of neural network systems is an advantage.
Call 1: Project start autumn 2021
This project is in call 1, starting autumn 2021. Read about how to apply