Topic: epileptic patients

Hierarchical working memory refers to the human ability to group information on-the-fly into larger chunks, thereby overcoming the limited capacity of Working Memory (WM), as first suggested by George A. Miller in his 1956 paper. Although chunking has been explored experimentally in numerous studies since then, its neuronal underpinnings remain unknown to this day. In our work, we address this issue by proposing a novel Recurrent Neural Network (RNN) model that enables the spontaneous organization of memory into chunks within WM. Our model leverages specialized “chunking neurons” and long-term

Studying the neural mechanisms of encoding new memories is crucial for understanding and restoring cognitive function. In this study, we utilized functional connectivity metrics to identify hubs responsible for successful or unsuccessful encoding in a free recall task among eleven epileptic patients undergoing stereo EEG presurgical evaluation. Firstly, several types of functional connectivity metrics were calculated across standard EEG bands, ripples, and fast ripples for each patient, feature, encoded word and time window across all possible pairs of contacts. Whole-brain connectivity maps w