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Zhou X, Zhou S, Han Y, Zhu S. Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5241-5268. [PMID: 35430863 DOI: 10.3934/mbe.2022246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Lévy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.
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Affiliation(s)
- Xin Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Shangbo Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Yuxiao Han
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Shufang Zhu
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
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Tandoc MC, Bayda M, Poskanzer C, Cho E, Cox R, Stickgold R, Schapiro AC. Examining the effects of time of day and sleep on generalization. PLoS One 2021; 16:e0255423. [PMID: 34339459 PMCID: PMC8328323 DOI: 10.1371/journal.pone.0255423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/16/2021] [Indexed: 12/26/2022] Open
Abstract
Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for time of day apart from sleep. In follow-up experiments, we were unable to replicate the time of day effect for reasons that may relate to changes in category structure and task engagement. Despite this lack of consistency, we found a morning benefit for generalization when analyzing all the data from experiments with matched protocols (n = 136). We suggest that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.
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Affiliation(s)
- Marlie C. Tandoc
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mollie Bayda
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychology, University of California-Los Angeles, Los Angeles, California, United States of America
| | - Craig Poskanzer
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychology and Neuroscience, Boston College, Chestnut Hill, Massachusetts, United States of America
| | - Eileen Cho
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
| | - Roy Cox
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anna C. Schapiro
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Psychiatry, Beth Israel Deaconess Medical Center / Harvard Medical School, Boston, Massachusetts, United States of America
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Perceptual Learning with Complex Objects: A Comparison between Full-Practice Training and Memory Reactivation. eNeuro 2021; 8:ENEURO.0008-19.2021. [PMID: 33558270 PMCID: PMC7986539 DOI: 10.1523/eneuro.0008-19.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/12/2021] [Accepted: 01/30/2021] [Indexed: 12/03/2022] Open
Abstract
Perception improves with repeated exposure. Evidence has shown object recognition can be improved by training for multiple days in adults. Recently, a study of Amar-Halpert et al. (2017) has compared the learning effect of repetitive and brief, at-threshold training on a discrimination task and reported similar improvement in both groups. The finding is interpreted as evidence that memory reactivation benefits discrimination learning. This raises the question how this process might influence different perceptual tasks, including tasks with more complex visual stimuli. Here, this preregistered study investigates whether reactivation induces improvements in a visual object learning task that includes more complex visual stimuli. Participants were trained to recognize a set of objects during 5 d of training. After the initial training, a group was trained with repeated practice, the other a few near-threshold reactivation trials. In both groups, we found improved object recognition at brief exposure durations. Traditional intense training shows a daily improvement; however, the group with reactivation does not reach the same level of improvement. Our findings show that reactivation has a smaller effect relative to large amounts of practice.
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Xia X, Gui L, Yu F, Wu H, Wei B, Zhang YL, Zhan ZH. Triple Archives Particle Swarm Optimization. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:4862-4875. [PMID: 31613789 DOI: 10.1109/tcyb.2019.2943928] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.
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Tactile recognition of visual stimuli: Specificity versus generalization of perceptual learning. Vision Res 2018; 152:40-50. [DOI: 10.1016/j.visres.2017.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 10/30/2017] [Accepted: 11/16/2017] [Indexed: 11/19/2022]
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Beyeler M, Rokem A, Boynton GM, Fine I. Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies. J Neural Eng 2017; 14:051003. [PMID: 28612755 DOI: 10.1088/1741-2552/aa795e] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The 'bionic eye'-so long a dream of the future-is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.
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Affiliation(s)
- Michael Beyeler
- Department of Psychology, University of Washington, Seattle, WA, United States of America. Institute for Neuroengineering, University of Washington, Seattle, WA, United States of America. eScience Institute, University of Washington, Seattle, WA, United States of America
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