For the Vidi project ‘Top-down neuromodulation and bottom-up network computation,’ we seek a postdoc to study neuromodulators in efficient spike-coding networks. Using our lab’s data on dopamine, acetylcholine, and serotonin from the mouse barrel cortex, you’ll derive models connecting single cells, networks, and behavior.

The aim of this project is to explain the effects of neuromodulation on task performance in biologically realistic spiking recurrent neural networks (SRNNs). You will use the efficient spike coding framework, in which a network is not trained by a learning paradigm but deduced using mathematically rigorous rules that enforce efficient coding (i.e. maximally informative spikes). You will study how the network’s structural properties such as neural heterogeneity influence decoding performance and efficiency. You will incorporate realistic network properties of the (barrel) cortex based on our lab’s measurements and incorporate the cellular effects of dopamine, acetylcholine and serotonin we have measured over the past years into the network, to investigate their effects on representations, network activity measures such as dimensionality, and decoding performance. You will build on the single cell data, network models and analysis methods available in our group, and your results will be incorporated into our group’s further research to develop and validate efficient coding models of (somatosensory) perception. Therefore, we are looking for a team player who is willing to learn from the other group members and to share their knowledge with them.