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Relevant bibliographies by topics / Brain and learning

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Journal articles
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Academic literature on the topic 'Brain and learning'

Author: Grafiati

Published: 29 July 2024

Last updated: 30 July 2024

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Journal articles on the topic "Brain and learning"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Brain and learning"

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Evanshen, Pamela, and L. Phillips. "Brain Compatible Learning Environments." Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etsu-works/4368.

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Evanshen, Pamela. "Brain-compatible Learning Environments." Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etsu-works/4404.

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Thurston, Roy J. "Brain injury, memory and learning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0024/NQ49543.pdf.

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Brodnax, Rita M. "Brain compatible teaching for learning." [Bloomington, Ind.] : Indiana University, 2004. http://wwwlib.umi.com/dissertations/fullcit/3173526.

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Thesis (Ed. D.)--Indiana University, Dept. of Educational Leadership, 2004.
Title from PDF t.p. (viewed Dec. 8, 2008). Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1257. Chair: Ron Barnes.

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Parsapoor, Mahboobeh. "Brain Emotional Learning-Inspired Models." Licentiate thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25428.

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In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

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Nair, Hemanth P. "Brain imaging of developmental learning effects /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004348.

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Sperlich, Juntana Ginda. "Designing a brain-based learning environment." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3216.

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The purpose of this project was to develop a teacher friendly guide that would help teachers not only apply brain-based strategies in the classroom, but also to see results from transforming their classrooms into brain-based learning environments.

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Oscarsson, Jacob. "Exploring the Brain : Interactivity and Learning." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12329.

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This study has examined whether the use of an interactive 3D model of the human brain would be a more effective way of teaching it's anatomy in comparison to traditional book and paper-based techniques. The artefact created for the project was a three dimensional model of the brain made up of several anatomical structures that could be dissected to provide the user with a more accurate sense of the spatial relationships between each structure. The study conducted did not give sufficient information to accurately answer the research question, but interviews conducted during the experiment show interest in the technology. If developed, there could be potential for the use of this type of technology in the future.

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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.

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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The DI model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are be implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions. The context-integrating model emulates the brain’s ability to use contextual information to uniquely resolve the interpretation of ambiguous stimuli. A deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process is introduced. The model, referred to as the CINET, is implemented using a convolution neural network (CNN), which is shown to be ideal for combining target and context stimuli and for extracting coupled target-context features. The CINET parameters can be manipulated to simulate congruent and incongruent context environments and to manipulate target-context stimuli relationships. The formulation of the CINET is quite general; consequently, it is not restricted to stimuli in any particular sensory modality nor to the dimensionality of the stimuli. A broad range of experiments are designed to demonstrate the effectiveness of the CINET in resolving ambiguous visual stimuli and in improving the classification of non-ambiguous visual stimuli in various contextual environments. The fact that the performance improves through the inclusion of context can be exploited to design robust brain-inspired machine learning algorithms. It is interesting to note that the CINET is a classification model that is inspired by a combination of brain’s ability to integrate contextual information and the CNN, which is inspired by the hierarchical processing of visual information in the visual cortex. A convolution neural network (CNN) model, inspired by the hierarchical processing of visual information in the brain, is introduced to fuse information from an ensemble of multi-axial sensors in order to classify strikes such as boxing punches and taekwondo kicks in combat sports. Although CNNs are not an obvious choice for non-array data nor for signals with non-linear variations, it will be shown that CNN models can effectively classify multi-axial multi-sensor signals. Experiments involving the classification of three-axis accelerometer and three-axes gyroscope signals measuring boxing punches and taekwondo kicks showed that the performance of the fusion classifiers were significantly superior to the uni-axial classifiers. Interestingly, the classification accuracies of the CNN fusion classifiers were significantly higher than those of the DTW fusion classifiers. Through training with representative signals and the local feature extraction property, the CNNs tend to be invariant to the latency shifts and non-linear variations. Moreover, by increasing the number of network layers and the training set, the CNN classifiers offer the potential for even better performance as well as the ability to handle a larger number of classes. Finally, due to the generalized formulations, the classifier models can be easily adapted to classify multi-dimensional signals of multiple sensors in various other applications.

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Olsson, Joakim. "A Critique of the Learning Brain." Thesis, Uppsala universitet, Avdelningen för teoretisk filosofi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432105.

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The guiding question for this essay is: who is the learner? The aim is to examine and criticize one answer to this question, sometimes referred to as the theory of the learning brain, which suggests that the explanation of human learning can be reduced to the transmitting and storing of information in the brain’s formal and representational architecture, i.e., that the brain is the learner. This essay will argue that this answer is misleading, because it cannot account for the way people strive to learn in an attempt to lead a good life as it misrepresents the intentional life of the mind, which results in its counting ourselves out of the picture when it attempts to provide a scientific theory of the learning process. To criticize the theory of the learning brain, this essay will investigate its philosophical foundation, a theory of mind called cognitivism, which is the basis for the cognitive sciences. Cognitivism is itself built on three main tenets: mentalism, the mind-brain identity theory and the computer analogy. Each of these tenets will be criticized in turn, before the essay turns to criticize the theory of the learning brain itself. The focus of this essay is, in other words, mainly negative. The hope is that this criticism will lay the groundwork for an alternative view of mind, one that is better equipped to give meaningful answers to the important questions we have about what it means to learn, i.e., what we learn, how we do it and why. This alternative will emphasize the holistic and intentional character of the human mind, and consider the learning process as an intentional activity performed, not by isolated brains, but by people with minds that are extended, embodied, enacted and embedded in a sociocultural and physical context.

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Books on the topic "Brain and learning"

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Jensen, Eric. Brain-based learning. Del Mar, CA: Turning Point Pub., 1996.

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Jensen, Eric. The learning brain. San Diego, CA: Turning Point, 1995.

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Jensen, Eric. The learning brain. San Diego, CA: Turning Point Pub., 1994.

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Jensen, Eric. Brain-based learning. Del Mar, Calif: Turning Point, 1996.

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Hawkins, Jennifer Anne. Brain Plasticity and Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83530-9.

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Waterhouse, Alison. The Brain and Learning. Abingdon, Oxon ; New York : Routledge, [2020] | Series: The mental health and wellbeing teacher toolkit: Routledge, 2020. http://dx.doi.org/10.4324/9780429428050.

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Gayle, Gregory, ed. Designing brain-compatible learning. Arlington Heights, IL: SkyLight, 1998.

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Hart, Leslie A. Human brain & human learning. 3rd ed. Covington, Wash: Books for Educators, 2002.

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Jensen, Eric. Brain-based learning & teaching. Del Mar, CA: Turning Point Pub., 1995.

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Gayle, Gregory, ed. Designing brain-compatible learning. 2nd ed. Glenview, IL: SkyLight Professional Development, 2003.

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Book chapters on the topic "Brain and learning"

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de Asís Bravo-Rodríguez, Francisco, and Rocío Díaz-Aguilera. "Brain Tumors." In Learning Neuroimaging, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22999-2_1.

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Akshay, B. R., Sini Raj Pulari, T. S. Murugesh, and Shriram K. Vasudevan. "Brain tumor classification." In Machine Learning, 157–65. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781032676685-14.

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Coleman, John. "Learning, learning, learning." In The Psychology of the Teenage Brain, 35–49. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003331728-4.

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Haken, Hermann. "Learning." In Principles of Brain Functioning, 117–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-79570-1_9.

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Rodgers, David L., and Roberta L. Hales. "Brain-Based Learning." In Comprehensive Healthcare Simulation: ECMO Simulation, 43–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53844-6_5.

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Traub, Roger, and Andreas Draguhn. "Reinforcement Learning and Birdsong." In Brain Leitmotifs, 79–94. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-54537-5_6.

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Hu, Hang. "Observation of learning-brain." In Deeper Learning, 143–90. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003278702-8.

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Koopowitz, Harold. "Brain, Primitive, Flatworms." In Learning and Memory, 23–24. Boston, MA: Birkhäuser Boston, 1989. http://dx.doi.org/10.1007/978-1-4899-6778-7_9.

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Eggermont, Jos J. "Learning — The Cerebellum." In The Correlative Brain, 217–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51033-5_12.

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Eggermont, Jos J. "Learning — The Hippocampus." In The Correlative Brain, 233–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51033-5_13.

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Conference papers on the topic "Brain and learning"

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Wait, Sarah J., Justin Daho Lee, Michael Rappleye, and Andre Berndt. "Application of Machine Learning to Direct Calcium Indicator Engineering." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bm4b.2.

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Optimizing genetically encoded fluorescent indicators (GEFIs) is intellectually and experimentally taxing. We developed a machine learning (ML) platform to discover new variants of the calcium indicator GCaMP and illustrate ML’s ability to accelerate GEFI engineering.

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Wijethilake, Navodini, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So, Murat Yildirim, and Dushan N. Wadduwage. "DEEP2: Deep Learning Powered De-scattering with Excitation Patterning (DEEP)." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bw3b.3.

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We present DEEP2, a computational multiphoton microscope to image through scattering tissue. In DEEP2, temporally focused structured light excites deep tissue in wide-field, and deep learning reconstructs clean images from scattered measurements.

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Quarta, Eros, Anna L. Allegra Mascaro, Jessica Lucchesi, Costanza Campaioli, Leonardo Sacconi, and Francesco Saverio Pavone. "Mesoscale Imaging of Cortical Dynamics During Motor Skill Learning." In Optics and the Brain. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/brain.2018.btu2c.5.

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TELLI, Abderrahmane, and Allia Kacimi EL HASSAN. "BRAIN- BASED LEARNING STRATEGIES: THE BRAIN IS SOCIAL." In VII. International Research Congress of Contemporary Studies in Social Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/rimarcongress7-2.

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Brain research in recent decades has gained credibility and financial support and drawn attention to it, and the number of educators interested in studying brain research and trying to apply it in the educational field has increased. Harmonious with the brain - the social brain -, which is an educational strategy - if it is well exploited - and to benefit from it in activating the educational situation by activating its variables and putting them in a solution to get out of the indoctrination situation, in addition to the fact that it needs an actual programmed embodiment within the school space in order to achieve its goals This is based on the foundations of the theory of brain learning, as well as the means to support it. The study reached a set of results and suggestions that would contribute to facilitating the process of implementing educational strategies in harmony with the brain - the brain is social - and benefiting from it through:  The study reached to prove and confirm the results of the effectiveness of the theory in developing learning efficiency in the two dimensions of achievement and motivation to learn.  Attempting to build a teaching model using the strategies, principles and foundations of the theory of brain-based learning in general and the social brain in particular, and putting this model to the test

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O'Rourke, Eleanor, Erin Peach, Carol S. Dweck, and Zoran Popovic. "Brain Points." In L@S 2016: Third (2016) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2876034.2876040.

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Tahir, Waleed, Jiabei Zhu, Sreekanth Kura, Xiaojun Cheng, David Boas, and Lei Tian. "A Deep Learning Approach to 3D Segmentation of Brain Vasculature." In Optics and the Brain. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/brain.2019.bt2a.6.

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Lotfi, Ehsan, and M. R. Akbarzadeh-T. "Supervised brain emotional learning." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252391.

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Gao, Yuanyuan, Lora Cavuoto, Pingkun Yan, Uwe Kruger, Steven Schwaitzberg, Suvranu De, and Xavier Intes. "A deep learning approach to remove motion artifacts in fNIRS data analysis." In Optics and the Brain. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/brain.2020.bm2c.7.

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Liu, Chang, Jelena Platisa, Xin Ye, Allison M. Ahrens, Ichun Anderson Chen, Ian G. Davison, Vincent A. Pieribone, Jerry L. Chen, and Lei Tian. "DeepVID: A Self-supervised Deep Learning Framework for Two-photon Voltage Imaging Denoising." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/brain.2022.btu4c.4.

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Wu, Yichen, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, and Aydogan Ozcan. "Deep Learning-based Virtual Refocusing of Fluorescence Microscopy Images for Neuron Imaging in 3D." In Optics and the Brain. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/brain.2020.bw4c.6.

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Reports on the topic "Brain and learning"

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Drew, Donald A. Brain Behavior Evolution during Learning: Emergence of Hierarchical Temporal Memory. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada608125.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Chen, Barry Y. The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1335766.

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Hannas, William, Huey-Meei Chang, Daniel Chou, and Brian Fleeger. China's Advanced AI Research: Monitoring China's Paths to "General" Artificial Intelligence. Center for Security and Emerging Technology, July 2022. http://dx.doi.org/10.51593/20210064.

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China is following a national strategy to lead the world in artificial intelligence by 2030, including by pursuing “general AI” that can act autonomously in novel circumstances. Open-source research identifies 30 Chinese institutions engaged in one or more of this project‘s aspects, including machine learning, brain-inspired AI, and brain-computer interfaces. This report previews a CSET pilot program that will track China’s progress and provide timely alerts.

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Näslund-Hadley, Emma, Michelle Koussa, and Juan Manuel Hernández. Skills for Life: Stress and Brain Development in Early Childhood. Inter-American Development Bank, April 2021. http://dx.doi.org/10.18235/0003205.

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Learning to cope with disappointments and overcoming obstacles is part of growing up. By conquering some challenges, children develop resilience. Such normal stressors may include initiating a new activity or separation from parents during preschool hours. However, when the challenges in early childhood are intensified by important stressors happening outside their own lives, they may start to worry about the safety of themselves and their families. This may cause chronic stress, which interferes with their emotional, cognitive, and social development. In developing country contexts, it is especially hard to capture promptly the effects of stressors related to the COVID-19 pandemic on childrens cognitive and socioemotional development. In this note, we draw on the literature on the effect of stress on brain development and examine data from a recent survey of households with young children carried out in four Latin American countries to offer suggestions for policy responses. We suggest that early childhood and education systems play a decisive role in assessing and addressing childrens mental health needs. In the absence of forceful policy responses on multiple fronts, the mental health outcomes may become lasting.

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Dale, Naomi, Aneesa Khan, and Sophie Dale. Early intervention for vision and neurodevelopment in infants and very young children with visual impairment: a systematicreview. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0080.

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Review question / Objective: Research question - What is the effectiveness of Early Childhood Intervention (ECI) in the first 3 years of life? Population (P) Infants and very young children with diagnosed visual impairment. Intervention (I) ECI programmes that includes vision and developmental stimulation, play, learning and responsive parenting Comparison (C) Standard care or control Outcomes (O) Primary: Vision function or and/or neurodevelopment and/or parent-child interaction outcomes Secondary: Parental context factors eg parental wellbeing and mental health, parental satisfaction with service provision. Condition being studied: Childhood congenital or very early visual impairment arising from congenital disorders of the peripheral or anterior visual system or cerebral-based vision disorders. This includes all vision disorders of the globe, retina and anterior optic nerve and all vision disorders that are considered cerebral based along visual pathways that are retro-chiasmatic and include central brain regions and networks involved in vision processing.

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Abufhele, Alejandra, David Bravo, Florencia Lopez-Boo, and Pamela Soto-Ramirez. Developmental losses in young children from pre-primary program closures during the COVID-19 pandemic. Inter-American Development Bank, January 2022. http://dx.doi.org/10.18235/0003920.

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The learning and developmental losses from pre-primary program closures due to COVID-19 may be unprecedented. These disruptions early in life, when the brain is more sensitive to environmental changes, can be long-lasting. Although there is evidence about the effects of school closures on older children, there is currently no evidence on such losses for children in their early years. This paper is among the first to quantify the actual impact of pandemic-related closures on child development, in this case for a sample of young children in Chile, where school and childcare closures lasted for about a year. We use a unique dataset collected face-to-face in December 2020, which includes child development indicators for general development, language development, social-emotional development, and executive function. We are able to use a first difference strategy because Chile has a history of collecting longitudinal data on children as part of their national social policies monitoring strategy. This allows us to construct a valid comparison group from the 2017 longitudinal data. We find adverse impacts on children in 2020 compared to children interviewed in 2017 in most development areas. In particular, nine months after the start of the pandemic, we find a loss in language development of 0.25 SDs. This is equivalent to the impact on a childs language development of having a mother with approximately five years less education. Timely policies are needed to mitigate these enormous losses.

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‘The Centre for Attention Learning and Memory (CALM)’ – In conversation Dr. Joni Holmes. ACAMH, May 2021. http://dx.doi.org/10.13056/acamh.15780.

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The Centre for Attention Learning and Memory (CALM) Approach to Neurodevelopmental Research – MRC Cognition and Brain Sciences Unit University Of Cambridge. ACAMH, April 2021. http://dx.doi.org/10.13056/acamh.15509.

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Our thinking around neurodevelopmental disorders is undergoing a period of rapid change. The traditional approach, endorsed by classification systems such as the Diagnostic Statistical Manual, defines neurodevelopmental disorders such as autism and attention-deficit hyperactivity disorder (ADHD) as distinct categories.

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How can we model the brain when it goes awry? How Reinforcement Learning Models can shed light on Psychiatric Disorders that emerge during Development. ACAMH, January 2021. http://dx.doi.org/10.13056/acamh.14458.

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