In this project you will develop methods to distinguish cell type and cell state from single cell RNA-seq data that will be collected in associated experiments.
The goal is to model and understand plasticity effects in biological and artificial neural networks by studying learning dynamics in neural networks with plasticity rules and compare results with experiments.
The projects goal is to understand the structure and dynamics of neural network models trained on navigational task and compare with experimental studies.
Recent progress in both AI research and neuroscience open novel synergies for advancing both fields. Recent studies have shown that biologically realistic structures such as neural representations of navigation appear spontaneously during supervised learning of navigational tasks in artificial neural networks.
The last decade has seen an explosive development in the application of artificial intelligence (AI), in particular deep networks. Convolutional neural networks (CNNs) now outperform humans on many visual classification tasks. AI has clearly been inspired by the functioning of real brains, but AI may also help in unraveling how ‘biological intelligence’, that is, real brains work.
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.