Abstract
Working memory (WM) is an important part of cognitive activity. The WM system maintains information temporarily to be used in learning and decision-making. Recent studies of WM focused on positive feedback, but positive feedback models require fine tuning of the strength of the feedback and are sensitive to common perturbations. However, different people have different strength of the feedback and it is impossible to let every people have same network parameter. In this research, we proposed a new approach to understanding WM based on the theory that positive and negative feedback are closely balanced in neocortical circuits. Our experimental results demonstrated that the model does not need fine tuning parameter and can achieve the memory storage, memory association, memory updating and memory forgetting. Our proposed negative-derivative feedback model was shown to be more robust to common perturbations than previous models based on positive feedback alone.
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Acknowledgments
This work was supported by the NSFC Project (Project Nos. 61771146 and 61375122), (in part) by Shanghai Science and Technology Development Funds (Project Nos. 13dz2260200, 13511504300).
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Wei, H., Su, Z., Dai, D. (2018). Balanced Cortical Microcircuitry-Based Network for Working Memory. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_20
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DOI: https://doi.org/10.1007/978-3-030-01418-6_20
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