In this project you will implement and study neural networks that are able to extract causal dynamics and structures and are trained either with supervised or reinforcement learning.
Bioscience
In this project you will develop tools to analyze and understand neural dynamics from experiments recording large neural populations and implement the resulting insights into artificial neural networks.
In this project you will implement and study neural networks that are able to extract causal dynamics and structures and are trained either with supervised or reinforcement learning.
The projects goal is to understand the structure and dynamics of neural network models trained on navigational task and compare with experimental studies.
Recent advances in recording technology now allow simultaneous measurements of neural activity of several thousand neurons from connected brain areas. In this project, you will develop and apply methods to analyze such data for unique insights into neuron population dynamics underlying attention modulation and learning.
The project's goal is to develop and understand bio-inspired neural network models that overcome the inherent challenge of catastrophic forgetting in AI models.
In this project we will use computational modelling supported by genetic and multi-omics data to understand how genomic instability in defined cell populations can drive aging and neurodegeneration.
In this project, the candidate will join an interdisciplinary team of experimental and computational experts to decipher neural fingerprints of memory processing.
In the project we will, building on the Allen model, explore and develop a new improved model for the mouse visual cortex in close collaboration with researchers at the Allen Brain Institute.