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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Related Experiment Video

Updated: Jan 9, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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Orthogonal-Rotational Dynamics Supports Efficient Encoding and Updating for Streaming Information in Working Memory.

Binghao Yang1,2,3, Shan Yu4,2,3

  • 1Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

Researchers discovered an orthogonal-rotational framework in both recurrent neural networks and human brains for efficient working memory (WM) encoding and updating. This mechanism helps organize information streams, maintaining item order and updating memory effectively.

Keywords:
EEGRNN modelorthogonalityrotationworking memory

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Working memory (WM) is crucial for organizing, storing, and processing continuous information streams.
  • Efficient WM requires separating memory items, maintaining their temporal rank, and dynamically updating sequences.
  • Understanding the computational mechanisms of WM is essential for both biological and artificial systems.

Purpose of the Study:

  • To investigate the computational mechanisms underlying working memory (WM) encoding and updating.
  • To compare information representation in a recurrent neural network (RNN) model and human subjects during an N-back task.
  • To identify a potential common framework for information organization in biological and artificial neural networks.

Main Methods:

  • Analysis of information representation in an RNN model and human EEG signals during an N-back task.
  • Utilized an orthogonal-rotational dynamical framework to analyze memory encoding and updating processes.
  • Recorded EEG signals from prefrontal areas of 28 human participants (18 males).

Main Results:

  • Identified an orthogonal coding space in the RNN model where memory items occupy subspaces based on their rank.
  • Observed a rotational operation dynamically transferring information across subspaces, preserving order during updates.
  • Found similar orthogonal-rotational dynamics in human EEG signals, suggesting a shared mechanism for WM organization.
  • The framework enables a 'first in, first out' information storage and updating strategy.

Conclusions:

  • An orthogonal-rotational dynamical framework facilitates efficient memory encoding and updating in both RNNs and the human brain.
  • This coding strategy allows for the distinct separation of memory items while maintaining temporal rank and dynamic updating.
  • The findings suggest a novel, potentially universal mechanism for organizing information streams in WM for efficient online processing.
  • This mechanism may be utilized by both biological and artificial neural networks for optimal information storage and updating.