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Journal articles on the topic 'Brain and learning'

Author: Grafiati

Published: 29 July 2024

Last updated: 30 July 2024

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1

Alla, Sri Sai Meghana, and Kavitha Athota. "Brain Tumor Detection Using Transfer Learning in Deep Learning." Indian Journal Of Science And Technology 15, no. 40 (October 27, 2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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Manasa, P. Venkata Sai, J. Jeevitha, M. Lakshmi Chandana, M. Jeevana Sravanthi, and M. Ali Shaik. "Brain Tumor Radiogenomic Classification Using Deep Learning." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 1830–36. http://dx.doi.org/10.55248/gengpi.2023.4.33058.

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3

Agrawal, Madhav, and Arham Jain. "Deep Learning Techniques in Brain Cancer Detection." International Journal of Science and Research (IJSR) 12, no. 11 (November 5, 2023): 41–49. http://dx.doi.org/10.21275/sr231029151256.

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4

Binulal.KR, Binulal KR, and Dr Ampili Aravind. "Review of Related Literature on Brain Based Learning." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 179–80. http://dx.doi.org/10.15373/2249555x/july2013/54.

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A, Ms Vidhya, Dr Parameswari R, and Ms Sathya S. "Brain Tumor Classification Using Various Machine Learning Algorithms." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 258–70. http://dx.doi.org/10.9756/bp2019.1002/25.

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M, Poshitha, Nazia Sultana A, Hemathanmaya S S, Mahendra A, and J. C. Vasantha Kumar. "Brain Tumor and Alzheimer’s Detection using Deep Learning." International Journal of Research Publication and Reviews 4, no. 6 (June 13, 2023): 2654–57. http://dx.doi.org/10.55248/gengpi.4.623.46522.

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7

Collado, Miguel Á., Cristina M. Montaner, Francisco P. Molina, Daniel Sol, and Ignasi Bartomeus. "Brain size predicts learning abilities in bees." Royal Society Open Science 8, no. 5 (May 2021): 201940. http://dx.doi.org/10.1098/rsos.201940.

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When it comes to the brain, bigger is generally considered better in terms of cognitive performance. While this notion is supported by studies of birds and primates showing that larger brains improve learning capacity, similar evidence is surprisingly lacking for invertebrates. Although the brain of invertebrates is smaller and simpler than that of vertebrates, recent work in insects has revealed enormous variation in size across species. Here, we ask whether bee species that have larger brains also have higher learning abilities. We conducted an experiment in which field-collected individuals had to associate an unconditioned stimulus (sucrose) with a conditioned stimulus (coloured strip). We found that most species can learn to associate a colour with a reward, yet some do so better than others. These differences in learning were related to brain size: species with larger brains—both absolute and relative to body size—exhibited enhanced performance to learn the reward-colour association. Our finding highlights the functional significance of brain size in insects, filling a major gap in our understanding of brain evolution and opening new opportunities for future research.

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Suarez, Angelica Maria Sabando, Maria Elena Moya Martinez, and Luis Raul Meza Mendoza. "Brain and learning." International journal of social sciences and humanities 3, no. 2 (July 26, 2019): 128–32. http://dx.doi.org/10.29332/ijssh.v3n2.302.

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The brain is an important organ that directs all the actions of the body and the intervention that it has in human behavior, is fundamental for the analysis of the subject since by means of its study it can be analyzed its structure, functioning, coordination, and control. Exercises in different actions, where they link the knowledge of What is the brain?. What is learning?. And What is neuroscience? to recognize the impact they exert on the daily actions of the human being. The present work uses the bibliographic reference where the information will have sustained, which aims to define the importance of the Brain and its relationship in learning activities, through experience and knowledge. Finally, the conclusions of the work ha exposed, where technological and scientific advances have detailed with respect to the importance of the brain in the learning and teaching processes, from different sciences, understanding the importance and development of the knowledge.

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9

Ahr, Emmanuel, Grégoire Borst, and Olivier Houdé. "The Learning Brain." Zeitschrift für Psychologie 224, no. 4 (October 2016): 277–85. http://dx.doi.org/10.1027/2151-2604/a000263.

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Abstract. Reading is an example of complex learning specific to human beings. In readers, an area of the brain is dedicated to the visual processing of letters and words, referred to as the visual word form area (VWFA). The existence of this brain area is paradoxical. Reading is too recent to be a phylogenic product of Darwinian evolution. It likely develops with intense school training via a neuroplastic ontogenic process of neuronal recycling: neurons in the lateral occipitotemporal lobe originally tuned to the visual recognition of stimuli, such as faces, objects, and animals, will be recycled for the visual recognition of letters and words. Thus, the VWFA inherits the intrinsic properties of these neurons, notably, mirror generalization, a process (or heuristic) applied to all visual stimuli that enables the recognition of a stimulus irrespective of its left-right orientation. On its own, this inherited property is not adapted to reading because it makes children confuse mirror letters, such as b and d in the Latin alphabet. In this article, we present evidence that inhibitory control is critical to avoid mirror errors inherited from the neuronal recycling process by blocking the mirror generalization heuristic in the context of reading. We subsequently argue that the “neuronal recycling + inhibitory control” law constitutes a general law of the learning brain by demonstrating that it may also account for the development of numeracy.

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10

Thomas, Katherine Jean, and Carol Massee Holbert. "Whole-Brain Learning." AORN Journal 51, no. 1 (January 1990): 196–203. http://dx.doi.org/10.1016/s0001-2092(07)67254-8.

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11

Doyle, Phoebe. "Whole-brain learning." Early Years Educator 12, no. 2 (June 2010): 32–34. http://dx.doi.org/10.12968/eyed.2010.12.2.48438.

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12

Laine, Matti. "The Learning Brain." Brain and Language 71, no. 1 (January 2000): 132–34. http://dx.doi.org/10.1006/brln.1999.2232.

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13

Antonis, Theofilidis. "The Hypothesis of Unexplained Brain Damage and Learning Difficulties." Neuroscience and Neurological Surgery 8, no. 5 (May 10, 2021): 01–08. http://dx.doi.org/10.31579/2578-8868/165.

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Introduction: The term learning disabilities refers to a complex and multidimensional phenomenon that affects many thousands of students. Due to the rich symptomatology of learning difficulties and the increased differences between individuals, it has not been possible to analyze all cases exclusively from the perspective of the neuropsychological approach. Aim. The aim of this study was to present research conducted in the context of theories on the brain function of people with learning disabilities. Supporting the hypothesis of brain dysfunction. Methodology: Literature review was carried out in the web, which referred to researches on Special Learning Disabilities and the brain function associated with them. Results: Review of the literature highlighted key points of the relationship between learning difficulties and brain function. Brain dysfunction and the cognitive functions produced emerged as one of the key factors involved in learning disabilities. Many of the theories developed around the problems of children with learning disabilities have focused on specific areas of the brain that may be dysfunctional. Conclusions: The difficulty of locating obvious brain damage in individuals who have been characterized as dyslexic leads to the strengthening of the hypothesis of the existence of a slight or minimal brain damage that cannot be easily diagnosed and strengthens the hypothesis of an unexplained brain damage that could be heterogeneous groups of learning disabilities.

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Chan, John S. Y., Yifeng Wang, Jin H. Yan, and Huafu Chen. "Developmental implications of children’s brain networks and learning." Reviews in the Neurosciences 27, no. 7 (October 1, 2016): 713–27. http://dx.doi.org/10.1515/revneuro-2016-0007.

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AbstractThe human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children’s brains. We first focused on the general rules of brain network development and on the typical and atypical development of children’s brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

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Ramakrishnan, Jayalakshmi, and Dr R. Annakodi Dr.R.Annakodi. "Knowledge and Beliefs of Teachers Towards Brain Based Learning." Indian Journal of Applied Research 3, no. 11 (October 1, 2011): 154–56. http://dx.doi.org/10.15373/2249555x/nov2013/53.

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16

Mithapalli, Anita, Padmavathi R, R. K. Swetha, and Pushmitha C. "Brain Tumor and Parkinson’s Disease Detection using Deep Learning." International Journal of Research Publication and Reviews 5, no. 5 (May 2, 2024): 1036–39. http://dx.doi.org/10.55248/gengpi.5.0524.1116.

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17

Nishida, Satoshi, Yusuke Nakano, Antoine Blanc, Naoya Maeda, Masataka Kado, and Shinji Nishimoto. "Brain-Mediated Transfer Learning of Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5281–88. http://dx.doi.org/10.1609/aaai.v34i04.5974.

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The human brain can effectively learn a new task from a small number of samples, which indicates that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability.

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18

Isanović, Amina. "LEARNING AND TEACHING – BRAIN-BASED LEARNING MODEL." Zbornik radova Islamskog pedagoškog fakulteta u Zenici (Online), no. 7 (December 15, 2009): 107–19. http://dx.doi.org/10.51728/issn.2637-1480.2009.107.

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The paper present a new pedagogical issue known as brain-based learning. The main determinants of the phenomenon have been pointed out to question the possibilities of its methodical impact. The phrase brain-based learning is used as a term for the way of understandig a learning process. Various publications of an American author Eric Jensen's book with the same title were used for more detailed presentation. The central theme of the book is a relation betwen learning and the structure and function of a brain. Jensen believes that the results of a brain examination should serve as a basis for planning and realizing educational practice. However, the author of this paper seeks an answer to a question: To what extent can those results influence the methodical articulation of an educational process? Key words: brain-based learning, learning, teaching, educational process, brain examination

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19

Avarguès-Weber, Aurore, and Martin Giurfa. "Conceptual learning by miniature brains." Proceedings of the Royal Society B: Biological Sciences 280, no. 1772 (December 7, 2013): 20131907. http://dx.doi.org/10.1098/rspb.2013.1907.

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Concepts act as a cornerstone of human cognition. Humans and non-human primates learn conceptual relationships such as ‘same’, ‘different’, ‘larger than’, ‘better than’, among others. In all cases, the relationships have to be encoded by the brain independently of the physical nature of objects linked by the relation. Consequently, concepts are associated with high levels of cognitive sophistication and are not expected in an insect brain. Yet, various works have shown that the miniature brain of honeybees rapidly learns conceptual relationships involving visual stimuli. Concepts such as ‘same’, ‘different’, ‘above/below of’ or ‘left/right are well mastered by bees. We review here evidence about concept learning in honeybees and discuss both its potential adaptive advantage and its possible neural substrates. The results reviewed here challenge the traditional view attributing supremacy to larger brains when it comes to the elaboration of concepts and have wide implications for understanding how brains can form conceptual relations.

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20

Rashed, Essam A., Takashi Sakai, Jose Gomez-Tames, and Akimasa Hirata. "Brain AI: Deep Learning for Brain Stimulation." IEEE Pulse 10, no. 4 (July 2019): 3–5. http://dx.doi.org/10.1109/mpuls.2019.2923888.

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21

Azis, Abdul, and Suyadi Suyadi. "Arabic Learning Media Based on Neuroscience." Insyirah: Jurnal Ilmu Bahasa Arab dan Studi Islam 6, no. 1 (June 9, 2023): 34–44. http://dx.doi.org/10.26555/insyirah.v6i1.6731.

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Learning Arabic cannot be separated from learning media that can carry out the functions of the right brain and left brain, so learning Arabic will run effectively. This study uses a qualitative method with a library research approach. Researchers in data analysis use content analysis method where this method is carried out by identifying information objectively. The result of this discussion is that Arabic learning media that can optimize the left brain and right brain are varied. Neuroscience-based Arabic learning media. The first, using a flash card or word card media. Second, using audio-visual media. Both of these media have a function in learning listening competence, which can improve students' sense of hearing and familiarize them with receiving language input more often. In addition, this media utilizes all the purposes of language skills, namely listening, reading, writing and speaking. So that in the learning process, flash cards and audio-visual media optimize the functions of the two brains, namely the right and left brain.

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22

Lizana, Pablo. "Brain synchronization for learning." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2343. http://dx.doi.org/10.1121/1.3508296.

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23

Cramer, Tobias. "Learning with brain chemistry." Nature Materials 19, no. 9 (June 15, 2020): 934–35. http://dx.doi.org/10.1038/s41563-020-0711-y.

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24

Bray, Natasha. "The little learning brain." Nature Reviews Neuroscience 18, no. 5 (April 6, 2017): 263. http://dx.doi.org/10.1038/nrn.2017.47.

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25

Paulson, Pamela. "The Brain and Learning." Journal of Dance Education 2, no. 3 (July 2002): 81–83. http://dx.doi.org/10.1080/15290824.2002.10387213.

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26

南, 云. "Brain and Music Learning." Advances in Psychology 06, no. 01 (2016): 65–75. http://dx.doi.org/10.12677/ap.2016.61009.

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27

Tytell, E. "LEARNING WITHOUT A BRAIN." Journal of Experimental Biology 210, no. 21 (November 1, 2007): iv. http://dx.doi.org/10.1242/jeb.001164.

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28

Mannies, Nancy. "Brain Theory and Learning." Clearing House: A Journal of Educational Strategies, Issues and Ideas 60, no. 3 (November 1986): 127–30. http://dx.doi.org/10.1080/00098655.1986.9959303.

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29

Xu, Xing, Zhifeng Zhao, Rongpeng Li, and Honggang Zhang. "Brain-Inspired Stigmergy Learning." IEEE Access 7 (2019): 54410–24. http://dx.doi.org/10.1109/access.2019.2913182.

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30

Handayani, Baiq Sri, and A. D. Corebima. "Model brain based learning (BBL) and whole brain teaching (WBT) in learning." International Journal of Science and Applied Science: Conference Series 1, no. 2 (August 14, 2017): 153. http://dx.doi.org/10.20961/ijsascs.v1i2.5142.

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<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>

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31

Bick, Sarah K., Shaun R. Patel, Husam A. Katnani, Noam Peled, Alik Widge, Sydney S. Cash, and Emad N. Eskandar. "Caudate stimulation enhances learning." Brain 142, no. 10 (August 30, 2019): 2930–37. http://dx.doi.org/10.1093/brain/awz254.

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Neuromodulation offers the possibility of precise alteration of disordered neural circuits. In patients with depth electrodes implanted for seizure localization, Bick et al. show that caudate stimulation improves associative learning and modulates learning-related activity in dorsolateral prefrontal cortex. Caudate stimulation may be a promising treatment for memory disorders.

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32

Schmelzeisen, Antonie. "THE IMPACT OF SOCIAL LEARNING ON THE HUMAN BRAIN." Cortica 2, no. 1 (March 20, 2023): 170–74. http://dx.doi.org/10.26034/cortica.2023.3660.

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My brain, your brain, our brain are more than the sum of its parts. The when, where, and how of learning and memory taking place inside it still entails many hidden hypotheses to be investigated. The essay investigates how human brains encode their own learning and memory processes, how the topology of one's larger social network shows similar neural patterns to neural patterns of our friends and why safe learning environments are crucial.

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33

G. Maheswari and H. Indu. "Brain Activation Using Brain Gym for Effective Learning." Journal of Advanced Zoology 44, no. 3 (October 30, 2023): 1053–60. http://dx.doi.org/10.17762/jaz.v44i3.1356.

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This study attempts to find out the effectiveness of brain activation using brain gym training in learning educational psychology. Brain Gym comprised of six basic exercises that helps to improve cognitive function and learning. It consists of a series of simple body movements that help to cajole the two hemispheres of the brain into working in unison. This is an experimental study that compares the impact of two teaching approaches (brain gym and conventional method). The intervention lasted for two weeks. The post-test-only rotational group design study included 90 students, 45 in the experimental group (Brain gym) and 45 in the control group (Conventional method). To ensure the similarity of the groups an entry level test was conducted and the scores obtained were compared using t test. The data were analysed using SPSS and the results indicated that brain gym had a significant effect on learning.

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34

Miall, R. Chris, and Joseph Galea. "Cerebellar damage limits reinforcement learning." Brain 139, no. 1 (January 2016): 4–7. http://dx.doi.org/10.1093/brain/awv343.

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35

Keri, S. "Classification learning in Alzheimer's disease." Brain 122, no. 6 (June 1, 1999): 1063–68. http://dx.doi.org/10.1093/brain/122.6.1063.

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36

Vinod, Manvika. "Detection of Brain Tumor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (October 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26485.

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Brain tumor detection and segmentation are important tasks in medical image analysis. This project is about creating an image classification model to detect whether an MRI image of a brain has a tumor or not. The model is created using Fast ai, which is a high-level deep learning library built on top of Py Torch. The dataset used in this project contains MRI images of brains with and without tumors. The model is trained using transfer learning with ResNet18 and ResNet34 as the base architectures. After training the model, it is exported and used to make predictions on new images using a simple web interface built with widgets. Keywords: Brain tumor detection, segmentation, medical image analysis, Fast.ai, deep learning, transfer learning, ResNet18, ResNet34, MRI images, web interface

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37

Dr Smitha M Reddy, Reshma HS,. "Inlfuence of Brain-Based Learning Stratgies on Academic Motivation, Stress and Self-Esteem of High School Students in North Banagalore." Psychology and Education Journal 58, no. 2 (February 20, 2021): 6329–32. http://dx.doi.org/10.17762/pae.v58i2.3154.

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Brain-Based Learning Strategy stimulates the whole brain for effective function which results in greater academic progress. This being the case it is bound to result in academic motivation, removal of stress and an increase in the self-esteem of students without any doubt. Brain-Based Learning Strategy provides a safe and threat-free environment whereby the meaningful presentation of content prepares the learners’ brains to store, process and retrieve the information in a soothing way. The main objective of this paper is to study the influence of brain-based learning strategies on the academic motivation, stress and self-esteem of high school students in North Bangalore, identity the factors of brain-based learning which influence learning process among high school students and then move on to identifying the factors of motivation, stress and self-esteem which influence the academic performance of high school students with respect to brain-based learning. The results of the study have confirmed that brain-based learning would result in motivation, removal of stress and higher self-esteem thereby resulting in improved academic performance.

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38

Kawato, Mitsuo. "Learning in robotics. Motor Learning in the Brain." Journal of the Robotics Society of Japan 13, no. 1 (1995): 11–19. http://dx.doi.org/10.7210/jrsj.13.11.

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39

Ge, Yuheng. "Federated learning-based machine learning for predicting brain tumor." Applied and Computational Engineering 46, no. 1 (March 15, 2024): 67–71. http://dx.doi.org/10.54254/2755-2721/46/20241096.

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The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.

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40

Muslimovic, D., B. Post, J. D. Speelman, and B. Schmand. "Motor procedural learning in Parkinson's disease." Brain 130, no. 11 (April 5, 2007): 2887–97. http://dx.doi.org/10.1093/brain/awm211.

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41

Lotze, M. "Motor learning elicited by voluntary drive." Brain 126, no. 4 (April 1, 2003): 866–72. http://dx.doi.org/10.1093/brain/awg079.

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42

Stern, Peter. "Brain region coordination in learning." Science 372, no. 6537 (April 1, 2021): 43.14–45. http://dx.doi.org/10.1126/science.372.6537.43-n.

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43

Chávez, Elvira Judith Mero, Willian Liborio Delgado Pibaque, Washington Javier Mero Chávez, and María Mercedes López López. "Learning problems on brain disorders." International research journal of engineering, IT & scientific research 5, no. 5 (August 29, 2019): 8–15. http://dx.doi.org/10.21744/irjeis.v5n5.723.

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The learning problems have aimed at identifying the disorders or disorders of the brain, that has perceived as conflicts of the cognitive system prevent the acquisition of significant knowledge. Among the most frequent problems are mentioned three: Dyslexia, dysgraphia, and dyscalculia that make it impossible for students to increase reading, writing and mathematical processes, causing disadvantages in the development of skills and abilities. The need for teachers in pedagogical interventions has raised, since they work in conjunction with psychologists and psychologists in order to take control from the detection of a problem and progress, identifying weaknesses and strengths that facilitate the proper application of pedagogical techniques and methods. The methodology applied in this investigation was inductive-deductive, emphasizing the study of problems from the particular to the general and included techniques such as survey, interview, control cards, and observation card. This will have a significant impact on society as critical, analytical and reflective students have formed, who develop in the educational, family and social environment.

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44

Morgan, Gary. "Cognitive Development: The Learning Brain." International Journal of Language & Communication Disorders 45, no. 2 (March 2010): 262. http://dx.doi.org/10.3109/13682820903211091.

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45

Barinaga, Marcia. "Learning Defect Identified in Brain." Science 273, no. 5277 (August 16, 1996): 867–68. http://dx.doi.org/10.1126/science.273.5277.867.b.

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46

Chakraborty, Prabuddha, and Swarup Bhunia. "BINGO: brain-inspired learning memory." Neural Computing and Applications 34, no. 4 (October 20, 2021): 3223–47. http://dx.doi.org/10.1007/s00521-021-06484-8.

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AbstractStorage and retrieval of data in a computer memory play a major role in system performance. Traditionally, computer memory organization is ‘static’—i.e. it does not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, in the case of a content-operated memory (COM), the association of a data block with a search pattern (or cues) and the granularity (details) of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data—as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce BINGO, a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In BINGO, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/retention algorithms in BINGO, which integrate a formalized learning process. Using an operational model, we demonstrate that BINGO achieves an order of magnitude improvement in memory access times and effective storage capacity using the CIFAR-10 dataset and the wildlife surveillance dataset when compared to traditional content-operated memory.

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Barinaga, Marcia. "Learning Defect Identified in Brain." Science 273, no. 5277 (August 16, 1996): 867–68. http://dx.doi.org/10.1126/science.273.5277.867-b.

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Arab, F. "Memory, learning and vicarious brain." Modelling, Measurement and Control C 79, no. 4 (December 30, 2018): 155–61. http://dx.doi.org/10.18280/mmc_c.790401.

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Black, Sandra E. "Brain Stimulation, Learning, and Memory." New England Journal of Medicine 366, no. 6 (February 9, 2012): 563–65. http://dx.doi.org/10.1056/nejme1114531.

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Seger, Carol A., and Earl K. Miller. "Category Learning in the Brain." Annual Review of Neuroscience 33, no. 1 (June 2010): 203–19. http://dx.doi.org/10.1146/annurev.neuro.051508.135546.

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