Orthogonal-rotational Dynamics Supports Efficient Encoding and Updating for Streaming Information in Working Memory

We rely on the working memory (WM) to organize, store, and process the perpetual stream of information received during our interaction with the world. Efficient encoding and rapid processing of WM requires a framework that (1) distinctly separates individual memory items while accurately maintaining their temporal rank, and (2) flexibly updates the sequence by discarding no-longer-needed items and accommodating newly arrived ones. To investigate the computational mechanisms underlying this functional implementation of WM, we analyzed the information representation in both a recurrent neural network (RNN) model and human subjects (n=28, 18 males) engaged in the same N -back WM task, which necessitates continuous encoding and updating of memory items. We discovered that an orthogonal-rotational dynamical framework facilitates memory encoding and updating, allowing both the RNN and human brain to efficiently organize memory items. In the RNN model, we identified an orthogonal coding space where each memory item occupies a subspace corresponding to its ordinal rank. A rotational operation dynamically transfers information across these subspaces, updating memory while preserving their internal order. Overall, this orthogonal-rotational framework enables the network to store the information in a “first in, first out” manner. Remarkably, we also observed similar orthogonal-rotational dynamics in EEG signals recorded from the prefrontal areas of human participants engaged in the same task. These findings suggest a novel mechanism underlying the brain's ability to efficiently organize information stream for efficient “online” processing and indicate that this coding strategy may be utilized by both biological and artificial neural networks for optimal information storage and updating. Significance Statement In our interactions with the world, the brain receives information as continuous streams, which we temporarily store and process using the working memory (WM). Efficient “online” processing in WM is therefore crucial, which requires preserving the order of incoming information and discarding no-longer-needed items to accommodate new ones simultaneously. The mechanisms by which the brain achieves these functions remain an open question. To address this, we conducted a comparative analysis of the neural activity in both a recurrent neural network (RNN) model and human brain dynamics engaged in a N -back WM task and found the orthogonal-rotational dynamics in both RNN and brain activities. These findings revealed a novel mechanism underlying the brain's ability to efficiently organize information streams for efficient“online” processing.