1 Current information

We meet every week in the corner office”. You can see the schedule on the calendar.

In lab meetings, we discuss organisatory issues and everyone can bring up potential topics or problems to discuss.

Additionally, we do presentations of our work, discuss papers, etc. Currently, the presentations are informal progress reports, one per meeting. The order is alphabetical according to first name.

Please upload your progress report to this online shared folder.

2 Past: journal club reading list

Reading list for a potential/future journal club (will be updated as I come across new papers that may be useful; suggestions are welcome!)

2.1 fMRI methods

Margulies et al., 2016: Hierarchy of areas based on the resting-state data: https://www.pnas.org/doi/10.1073/pnas.1608282113
Glasser parcellation of the cortex, which everyone is using now: https://www.nature.com/articles/nature18933
Multiple comparison correction methods for fMRI (no specific paper yet)
HCP processing pipeline and CIFTI format (alternative to fmriprep and state-of-the-art for 7 T preprocessing): https://www.sciencedirect.com/science/article/pii/S1053811913005053?via%3Dihub
Resting-state connectivity of subcortical structures (including the claustrum) https://www.jneurosci.org/content/43/39/6609
Neuroquery https://elifesciences.org/articles/53385
Neurodesk https://www.nature.com/articles/s41592-023-02145-x
White matter activations https://www.pnas.org/doi/10.1073/pnas.2219666120

2.2 Subjective experience

Self et al., 2019: ephys signatures of figure-ground segmentation in macaque V1: https://www.sciencedirect.com/science/article/pii/S0960982219301551
Recent findings from Tsao lab: https://www.pnas.org/doi/10.1073/pnas.2221122120
DMN and awareness: https://www.nature.com/articles/s41467-022-34410-6
Perceptography: https://www.biorxiv.org/content/10.1101/2022.10.24.513337v2
Perceptography in humans using visual illusions: https://www.science.org/doi/full/10.1126/sciadv.adj3906

2.3 General methods

Statistical power (small sample sizes): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6267527/
Statistical power: https://elifesciences.org/articles/62461
Experimental methods: https://www.frontiersin.org/articles/10.3389/fnhum.2018.00421/full
Scientific writing: https://stevenpinker.com/files/pinker/files/descioli_pinker_2021_piled_modifiers.pdf
Randomization techniques for statistical inference: permutation, bootstrapping, Monte Carlo simulations (no specific paper yet)

2.4 AI

https://www.sciencedirect.com/science/article/pii/S2589004222011853?via%3Dihub