The effects of the post-delay epochs on working memory error reduction

Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory r...

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Published inPLoS computational biology Vol. 21; no. 5; p. e1013083
Main Authors Ye, Zeyuan, Li, Haoran, Tian, Liang, Zhou, Changsong
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 13.05.2025
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Abstract Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color delayed-response task, where certain colors (referred to as common colors) were more frequently presented for memorization. We found that the trained RNNs reduced memory errors for common colors by decoding a broader range of neural states into these colors through the post-delay epochs. This decoding process was driven by convergent neural dynamics and a non-dynamic, biased readout process during the post-delay epochs. Our findings highlight the importance of post-delay epochs in working memory and suggest that neural systems adapt to environmental statistics by using multiple mechanisms across task epochs.
AbstractList Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color delayed-response task, where certain colors (referred to as common colors) were more frequently presented for memorization. We found that the trained RNNs reduced memory errors for common colors by decoding a broader range of neural states into these colors through the post-delay epochs. This decoding process was driven by convergent neural dynamics and a non-dynamic, biased readout process during the post-delay epochs. Our findings highlight the importance of post-delay epochs in working memory and suggest that neural systems adapt to environmental statistics by using multiple mechanisms across task epochs.
Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color delayed-response task, where certain colors (referred to as common colors) were more frequently presented for memorization. We found that the trained RNNs reduced memory errors for common colors by decoding a broader range of neural states into these colors through the post-delay epochs. This decoding process was driven by convergent neural dynamics and a non-dynamic, biased readout process during the post-delay epochs. Our findings highlight the importance of post-delay epochs in working memory and suggest that neural systems adapt to environmental statistics by using multiple mechanisms across task epochs. In daily life, we often need to store information temporarily (during a delay epoch) and retrieve it later when required. While this ability may seem simple, it poses significant challenges at the neural level. Neural activity is inherently highly variable (“noisy”), so how can we maintain accurate memory despite this variability? Previous research has primarily focused on neural processes during the delay epoch, however, the neural processes after delay (post-delay epochs) remain poorly studied. The post-delay epochs are critical for information retrieval—for instance, they are time periods when we write down something we just heard of, or, in a laboratory setting, when an animal makes a saccade to indicate a memorized color to earn a reward. In this study, we investigated the computational role of the post-delay epochs in helping noisy neural networks reduce memory errors. Our findings revealed that post-delay processes adapt to environmental statistics, and identified key neural mechanisms that reduce memory errors, highlighting the importance of post-delay epochs in supporting robust working memory.
Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color delayed-response task, where certain colors (referred to as common colors) were more frequently presented for memorization. We found that the trained RNNs reduced memory errors for common colors by decoding a broader range of neural states into these colors through the post-delay epochs. This decoding process was driven by convergent neural dynamics and a non-dynamic, biased readout process during the post-delay epochs. Our findings highlight the importance of post-delay epochs in working memory and suggest that neural systems adapt to environmental statistics by using multiple mechanisms across task epochs.Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive cues and generate responses. However, the computational and neural mechanisms that underlie these post-delay epochs to support robust memory remain poorly understood. To address this, we trained recurrent neural networks (RNNs) on a color delayed-response task, where certain colors (referred to as common colors) were more frequently presented for memorization. We found that the trained RNNs reduced memory errors for common colors by decoding a broader range of neural states into these colors through the post-delay epochs. This decoding process was driven by convergent neural dynamics and a non-dynamic, biased readout process during the post-delay epochs. Our findings highlight the importance of post-delay epochs in working memory and suggest that neural systems adapt to environmental statistics by using multiple mechanisms across task epochs.
Audience Academic
Author Li, Haoran
Tian, Liang
Zhou, Changsong
Ye, Zeyuan
AuthorAffiliation 5 Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, China
6 Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University, Hong Kong, China
2 Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Hong Kong Baptist University, Hong Kong, China
4 Department of Physics, Washington University in St. Louis, St. Louis, Missouri, United States of America
1 Department of Physics, Hong Kong Baptist University, Hong Kong, China
7 Life Science Imaging Centre, Hong Kong Baptist University, Hong Kong, China
UT Austin: The University of Texas at Austin, UNITED STATES OF AMERICA
3 Institute of Transdisciplinary Studies, Hong Kong Baptist University, Hong KongChina
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Snippet Accurate retrieval of the maintained information is crucial for working memory. This process primarily occurs during post-delay epochs, when subjects receive...
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SubjectTerms Biology and Life Sciences
Computational Biology
Computer and Information Sciences
Humans
Male
Memory, Short-Term - physiology
Models, Neurological
Neural circuitry
Neural Networks, Computer
Neurological research
Physical Sciences
Research and Analysis Methods
Short-term memory
Social Sciences
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Title The effects of the post-delay epochs on working memory error reduction
URI https://www.ncbi.nlm.nih.gov/pubmed/40359421
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Volume 21
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