Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems

Humans and other animals effortlessly generalize prior knowledge to solve novel problems, by abstracting common structure and mapping it onto new sensorimotor specifics. To investigate how the brain achieves this, in this study, we trained mice on a series of reversal learning problems that shared the same structure but had different physical implementations. Performance improved across problems, indicating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across problems despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each problem. This was true for both representations of the events that comprised each trial and those that integrated choices and outcomes over multiple trials to guide an animal’s decisions. These data suggest that prefrontal cortex and hippocampus play complementary roles in generalization of knowledge: PFC abstracts the common structure among related problems, and hippocampus maps this structure onto the specifics of the current situation

Complementary task representations in hippocampus and prefrontal cortex for generalising the structure of problems

Data folder includes two subfolders: 1) training: behavioural training data for each animal and 2) data_recordings: data from the recording phase. data_recordings includes raw firing rates and trial information for hippocampal (HP.mat) and prefrontal (PFC.mat) animals. It also includes two subfolders. after PCA subfolder includes HP_dlc_pca.mat and PFC_dlc_pca.mat files which are residual firing rates after accounting for movement related features using DeepLabCut. behaviour subfolder include raw behavioural data from the recording phase. the data is organised in such a way that publicly available code can be used to reproduce the figures from the paper. Link to the code is here https://github.com/veronikasamborska1994/notebooks_paper