"# TransitiveInference"

This is the code associated to the paper "Memory reactivation in slow wave sleep enhances relational learning". You can find it here: https://doi.org/10.1101/2022.03.29.486197

Abstract Memories are often strengthened by sleep, which can also boost integration and relational learning. This process can be facilitated by a technique called targeted memory reactivation (TMR), which involves re-applying cues that were associated with learned material in wake during subsequent sleep. We tested whether TMR during slow wave sleep increases the solving of inference pairs in a transitive inference task. Because the slow oscillation up-state is thought to be more important for plasticity, we also asked whether stimulation at this phase is more beneficial. Our data show that TMR can boost inference on the most difficult pairs, but only when presented during the down-to-up transition of the slow oscillation. Such stimulation was associated with classifiable replay, whereas stimulation of the up-to-down transition produced no apparent replay and led to below-chance performance. These findings demonstrate that targeted memory replay in sleep can play a role in integration and relational learning.

Behavioural dataset can be found in OSF repository Closed_loop TMR Relational Learning (DOI 10.17605/OSF.IO/BQAHC)

Dependencies:

EEG analysis: eeglab (https://sccn.ucsd.edu/eeglab/index.php), fieldtrip (https://www.fieldtriptoolbox.org/)