Since the beginning of the project, we have published the framework behind the project. The paper is already highly cited. A second theoretical paper going further, based on our results, on the role of brain fluctuations in visual processing in a naturalistic environment will be published soon.
We have also run an experiment using functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) combined with 52 subjects.
As predicted by the framework, we found that fast oscillations (i.e. gamma) and two slow oscillations (here in the alpha band, between 8 and 14Hz) are associated with specific layers of the cortex (which contains around 6 layers) and are involved in specific processing of the stimuli presented to the participants. The paper has been submitted to a journal for peer review. Another paper is in preparation regarding the link between the communication between brain regions as observed with fMRI and the oscillations at different frequencies. In addition, a new statistical tool (which allows one to determine whether the results observed can be generalized to the entire population) had to be developed and will be published soon.
We have also analyzed data obtained in Monkey revealing similar results as well as the interaction between the oscillations at different frequencies in the different layers of the cortex.
We have performed the fMRI and Magnetoencephalography (MEG) experiments in 45 participants aimed at determining whether the slow oscillations control the flow of information in the visual network. We used attention (participants were asked to attend to different parts of the visual field without moving their eyes) and prediction (each stimulus presented was predicting the features of the next one with a high probability) tasks.
We were able to track where the attention spotlight (where participants attended without moving their eyes) of participants and we showed that this attention spotlight was moving around at a slow rhythm. We also obtained results showing our ability to decode the content of the predictions regarding the next stimulus of participants. Several papers containing these results will be submitted for peer review soon.
We also found results showing the importance of alpha oscillations (8-14Hz) in preventing the transfer of irrelevant information in brain networks as predicted by our framework. These results will fuel the current very hot debate about the link between alpha oscillations and functional inhibition.
Finally, we obtained groundbreaking results using spiking neural networks performing attention tasks as our participants. Implementing alpha oscillations in these networks improve their performance. In addition, we observed that alpha oscillations emerge naturally in these networks when we reproduce the connectivity observed in the visual network of mammals' brains.
