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Journal articles on the topic 'Brain – Models'

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Author: Grafiati

Published: 4 June 2021

Last updated: 24 January 2023

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1

Asaro, Peter. "Working Models and the Synthetic Method." Science & Technology Studies 19, no. 1 (January 1, 2006): 12–34. http://dx.doi.org/10.23987/sts.55200.

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This article examines the construction of electronic brain models in the 1940s as an instance of “working models” in science. It argues that the best way to understand the scientific role of these synthetic brains is through combining aspects of the “models as mediators” approach (Morgan and Morrison, 1999) and the “synthetic method” (Cordeschi, 2002). Taken together these approaches allow a fuller understanding of how working models functioned within the brain sciences of the time. This combined approach to understanding models is applied to an investigation of two electronic brains built in the late 1940s, the Homeostat of W. Ross Ashby, and the Tortoise of W. Grey Walter. It also examines the writings of Ashby, a psychiatrist and leading proponent of the synthetic brain models, and Walter, a brain electro-physiologist, and their ideas on the pragmatic values of such models. I conclude that rather than mere toys or publicity stunts, these electronic brains are best understood by considering the roles they played as mediators between disparate theories of brain function and animal behavior, and their combined metaphorical and material power.

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2

Willis, J. B. "Models of brain function." Endeavour 16, no. 1 (January 1992): 46. http://dx.doi.org/10.1016/0160-9327(92)90131-8.

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3

Barinaga, M. "Neuroscience models the brain." Science 247, no. 4942 (February 2, 1990): 524–26. http://dx.doi.org/10.1126/science.2300812.

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4

Scherg, M., and J. S. Ebersole. "Models of brain sources." Brain Topography 5, no. 4 (1993): 419–23. http://dx.doi.org/10.1007/bf01128700.

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5

Alicea, B., and J. Parent. "Meta-brain Models: biologically-inspired cognitive agents." IOP Conference Series: Materials Science and Engineering 1261, no. 1 (October 1, 2022): 012019. http://dx.doi.org/10.1088/1757-899x/1261/1/012019.

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Abstract Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.

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6

Burke, David C. "Models of brain injury rehabilitation." Brain Injury 9, no. 7 (January 1995): 735–43. http://dx.doi.org/10.3109/02699059509008229.

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7

Miarka, Lauritz, and Manuel Valiente. "Animal models of brain metastasis." Neuro-Oncology Advances 3, Supplement_5 (November 1, 2021): v144—v156. http://dx.doi.org/10.1093/noajnl/vdab115.

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Abstract Modeling of metastatic disease in animal models is a critical resource to study the complexity of this multi-step process in a relevant system. Available models of metastatic disease to the brain are still far from ideal but they allow to address specific aspects of the biology or mimic clinically relevant scenarios. We not only review experimental models and their potential improvements but also discuss specific answers that could be obtained from them on unsolved aspects of clinical management.

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8

Aligholi, Hadi, and Maryam Safahani. "Experimental Models of Brain Injury." Neuroscience Journal of Shefaye Khatam 3, no. 2 (June 1, 2015): 69–76. http://dx.doi.org/10.18869/acadpub.shefa.3.2.69.

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9

Finkel, Leif H. "Neuroengineering Models of Brain Disease." Annual Review of Biomedical Engineering 2, no. 1 (August 2000): 577–606. http://dx.doi.org/10.1146/annurev.bioeng.2.1.577.

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10

Tupper, D. E. "Models of Brain Injury Rehabilitation." Neurology 39, no. 12 (December 1, 1989): 1649. http://dx.doi.org/10.1212/wnl.39.12.1649-a.

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11

Friston, Karl. "Hierarchical Models in the Brain." PLoS Computational Biology 4, no. 11 (November 7, 2008): e1000211. http://dx.doi.org/10.1371/journal.pcbi.1000211.

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12

Laurer, Helmut L., and Tracy K. McIntosh. "Experimental models of brain trauma." Current Opinion in Neurology 12, no. 6 (December 1999): 715–21. http://dx.doi.org/10.1097/00019052-199912000-00010.

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13

LIGHTHALL, JAMES W., C. EDWARD DIXON, and THOMAS E. ANDERSON. "Experimental Models of Brain Injury." Journal of Neurotrauma 6, no. 2 (January 1989): 83–97. http://dx.doi.org/10.1089/neu.1989.6.83.

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14

McDermott, Josh. "Brain models: the next generation." Nature Neuroscience 5, no. 9 (September 2002): 829. http://dx.doi.org/10.1038/nn0902-829.

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15

Downing, Keith L. "Predictive models in the brain." Connection Science 21, no. 1 (March 2009): 39–74. http://dx.doi.org/10.1080/09540090802610666.

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16

Cutsuridis, Vassilis, Tjitske Heida, Wlodek Duch, and Kenji Doya. "Neurocomputational models of brain disorders." Neural Networks 24, no. 6 (August 2011): 513–14. http://dx.doi.org/10.1016/j.neunet.2011.03.016.

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17

Long, Donlin M. "Probabilistic Models of the Brain." Neurosurgery Quarterly 12, no. 4 (December 2002): 312. http://dx.doi.org/10.1097/00013414-200212000-00005.

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18

Breakspear, Michael, and Stuart Knock. "Kinetic Models of Brain Activity." Brain Imaging and Behavior 2, no. 4 (September 26, 2008): 270–88. http://dx.doi.org/10.1007/s11682-008-9033-4.

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19

Arbib, Michael. "Modular models of brain function." Scholarpedia 2, no. 3 (2007): 1869. http://dx.doi.org/10.4249/scholarpedia.1869.

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20

Skyttner, Lars. "Brain cybernetics ‐ models and theories." Kybernetes 27, no. 8 (November 1998): 882–99. http://dx.doi.org/10.1108/03684929810240329.

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21

Golden, Charles J. "Computer models and the brain." Computers in Human Behavior 1, no. 1 (January 1985): 35–48. http://dx.doi.org/10.1016/0747-5632(85)90005-6.

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22

Laurer, Helmut L., Philipp M. Lenzlinger, and Tracy K. McIntosh. "Models of Traumatic Brain Injury." European Journal of Trauma 26, no. 3 (June 1, 2000): 95–110. http://dx.doi.org/10.1007/s000680050007.

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23

Virasoro, M. A. "Disordered models of the brain." Computer Physics Communications 56, no. 1 (November 1989): 93–100. http://dx.doi.org/10.1016/0010-4655(89)90055-6.

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24

Riordan, Ruben. "Brain Cellular Senescence in Mouse Models of Alzheimer's Disease." Innovation in Aging 5, Supplement_1 (December 1, 2021): 929. http://dx.doi.org/10.1093/geroni/igab046.3363.

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Abstract Abstract The accumulation of senescent cells contributes to aging pathologies, including neurodegenerative diseases, and its selective removal improves physiological and cognitive function in wild type mice as well as in Alzheimer’s disease (AD) models. AD models recapitulate some, but not all components of disease and do so at different rates. Whether brain cellular senescence is recapitulated in some or all AD models, and whether the emergence of cellular senescence in AD mouse models occurs before or after the expected onset of AD-like cognitive deficits in these models is not yet known. The goal of this study was to identify mouse models of AD and AD-related dementias that develop measurable markers of cellular senescence in brain and thus may be useful to study the role of cellular senescence in these conditions. We measured levels of cellular senescence markers in brains of P301S(PS19), P301L, hTau, and 3xTg-AD mice that model amyloidopathy and/or tauopathy in AD and related dementias, and in wild type, age-matched control mice for each strain. Expression of cellular senescence markers in brains of transgenic P301L and 3xTg-AD mice was largely indistinguishable from that in WT control age-matched mice. In contrast, markers of cellular senescence were significantly increased in brains of transgenic P301S and hTau mice as compared to WT control mice at the expected time of onset of AD-like cognitive deficits. Taken together, our data suggest that P301S(PS19) and hTau mice may be useful for the study of brain cellular senescence in tauopathies including, but not limited to, AD.

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25

Bullmore, Edward T., and Danielle S. Bassett. "Brain Graphs: Graphical Models of the Human Brain Connectome." Annual Review of Clinical Psychology 7, no. 1 (April 27, 2011): 113–40. http://dx.doi.org/10.1146/annurev-clinpsy-040510-143934.

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26

Martinez, Aleix M. "Computational Models of Face Perception." Current Directions in Psychological Science 26, no. 3 (June 2017): 263–69. http://dx.doi.org/10.1177/0963721417698535.

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Faces are one of the most important means of communication for humans. For example, a short glance at a person’s face provides information about his or her identity and emotional state. What are the computations the brain uses to acquire this information so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research tests the hypothesis that this algorithm is tasked with solving the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading features that are invariant to facial expression, pose, and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose, and illumination. If one defines the physics equations that render an image under different identities, expressions, poses, and illuminations, then gaining invariance to these factors can be readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust, and efficient as humans.

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27

Basheer, Faiza, Poshmaal Dhar, and Rasika M. Samarasinghe. "Zebrafish Models of Paediatric Brain Tumours." International Journal of Molecular Sciences 23, no. 17 (August 31, 2022): 9920. http://dx.doi.org/10.3390/ijms23179920.

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Paediatric brain cancer is the second most common childhood cancer and is the leading cause of cancer-related deaths in children. Despite significant advancements in the treatment modalities and improvements in the 5-year survival rate, it leaves long-term therapy-associated side effects in paediatric patients. Addressing these impairments demands further understanding of the molecularity and heterogeneity of these brain tumours, which can be demonstrated using different animal models of paediatric brain cancer. Here we review the use of zebrafish as potential in vivo models for paediatric brain tumour modelling, as well as catalogue the currently available zebrafish models used to study paediatric brain cancer pathophysiology, and discuss key findings, the unique attributes that these models add, current challenges and therapeutic significance.

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28

Sathasivam, Kirupa, Carl Hobbs, Laura Mangiarini, Amarbirpal Mahal, Mark Turmaine, Pat Doherty, Stephen W. Davies, and Gillian P. Bates. "Transgenic models of Huntington'sdisease." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 354, no. 1386 (June 29, 1999): 963–69. http://dx.doi.org/10.1098/rstb.1999.0447.

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Huntington'sdisease (HD) is an inherited neurodegenerative disorder caused by a CAG–polyglutamine repeat expansion. A mouse model of this disease has been generated by the introduction of exon 1 of the human HD gene carrying highly expanded CAG repeats into the mouse germ line (R6 lines). Transgenic mice develop a progressive neurological phenotype with a movement disorder and weight loss similar to that in HD. We have previously identified neuronal inclusions in the brains of these mice that have subsequently been established as the pathological hallmark of polyglutamine disease. Inclusions are present before symptoms, which in turn occur long before any selective neuronal cell death can be identified. We have extended the search for inclusions to skeletal muscle, which, like brain, contains terminally differentiated cells. We have conducted an investigation into the skeletal muscle atrophy that occurs in the R6 lines, (i) to provide possible insights into the muscle bulk loss observed in HD patients, and (ii) to conduct a parallel analysis into the consequence of inclusion formation to that being performed in brain. The identification of inclusions in skeletal muscle might be additionally useful in monitoring the ability of drugs to prevent inclusion formation in vivo .

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29

Pryce, Gareth, Zubair Ahmed, Deborah J. R. Hankey, Samuel J. Jackson, J. Ludovic Croxford, Jennifer M. Pocock, Catherine Ledent, et al. "Cannabinoids inhibit neurodegeneration in models of multiple sclerosis." Brain 126, no. 10 (October 2003): 2191–202. http://dx.doi.org/10.1093/brain/awg224.

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30

Li, Dan, and Michael C. Donohue. "Disease progression models for dominantly-inherited Alzheimer’s disease." Brain 141, no. 5 (April 23, 2018): 1244–46. http://dx.doi.org/10.1093/brain/awy089.

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31

Evans, Nathan J., and Eric-Jan Wagenmakers. "Theoretically meaningful models can answer clinically relevant questions." Brain 142, no. 5 (April 29, 2019): 1172–75. http://dx.doi.org/10.1093/brain/awz073.

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32

Tegeder, I., S. Meier, M. Burian, H. Schmidt, G. Geisslinger, and J. Lotsch. "Peripheral opioid analgesia in experimental human pain models." Brain 126, no. 5 (May 1, 2003): 1092–102. http://dx.doi.org/10.1093/brain/awg115.

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33

David Adelson, P. "Animal models of traumatic brain injury in the developing brain." Pathophysiology 5 (June 1998): 235. http://dx.doi.org/10.1016/s0928-4680(98)81210-8.

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34

Wang, Yingxu. "Neuroinformatics Models of Human Memory." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 1 (January 2013): 98–122. http://dx.doi.org/10.4018/jcini.2013010105.

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The human brain is a superbly marvelous and extremely complicated neurophysiological structure for generating natural intelligence that transforms cognitive information into colorful behaviors. The brain is the most complex and interesting objects in nature that requires rigorous scientific investigations by multidisciplinary methodologies and via transdisciplinary approaches where only low-level studies could not explain it. A fundamental problem and difficulty in contemporary brain science is the indistinguishable confusion of the cognitive mechanisms and neurophysiological structures of the kernel brain and its memories. This paper presents a set of formal neuroinformatics models of memory and a rigorous mapping between the cognitive functions of memory and their neurophysiological structures. The neurophysiological foundations of memory are rigorously described based on comprehensive cognitive models of memory. The cognitive architecture of human memory and its relationship to the intelligence power of the brain are logically analyzed. The cognitive roles of memory allocated in both cerebrum and cerebellum are revealed by mapping the functional models of memory onto corresponding neurophysiological structures of the brain. As a result, fundamental properties of memory and knowledge as well as their neurophysiological forms in the brain are systematically explained.

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35

Parnell, Miles, Li Guo, Mohamed Abdi, and M. Francesca Cordeiro. "Ocular Manifestations of Alzheimer’s Disease in Animal Models." International Journal of Alzheimer's Disease 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/786494.

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Alzheimer’s disease (AD) is the most common form of dementia, and the pathological changes of senile plaques (SPs) and neurofibrillary tangles (NFTs) in AD brains are well described. Clinically, a diagnosis remains a postmortem one, hampering both accurate and early diagnosis as well as research into potential new treatments. Visual deficits have long been noted in AD patients, and it is becoming increasingly apparent that histopathological changes already noted in the brain also occur in an extension of the brain; the retina. Due to the optically transparent nature of the eye, it is possible to image the retina at a cellular level noninvasively and thus potentially allow an earlier diagnosis as well as a way of monitoring progression and treatment effects. Transgenic animal models expressing amyloid precursor protein (APP) presenilin (PS) and tau mutations have been used successfully to recapitulate the pathological findings of AD in the brain. This paper will cover the ocular abnormalities that have been detected in these transgenic AD animal models.

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36

ERTİLAV, Kemal. "Traumatic brain injury models in rats." Journal of Cellular Neuroscience and Oxidative Stress 10, no. 3 (August 18, 2018): 781. http://dx.doi.org/10.37212/jcnos.610092.

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37

Frank, Guido K. W. "Brain Circuitry Models in Eating Disorders." Psychiatric Annals 41, no. 11 (November 1, 2011): 526–31. http://dx.doi.org/10.3928/00485713-20111017-05.

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38

Penny, William. "Bayesian Models of Brain and Behaviour." ISRN Biomathematics 2012 (October 23, 2012): 1–19. http://dx.doi.org/10.5402/2012/785791.

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This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, sensorimotor integration, and collective decision making. The review of brain models covers a range of spatial scales from synapses to neurons and population codes, but with an emphasis on models of cortical hierarchies. We describe a simple hierarchical model which provides a mathematical framework relating constructs in Bayesian inference to those in neural computation. We close by reviewing recent theoretical developments in Bayesian inference for planning and control.

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39

LOVE, B. C., and T. M. GURECKIS. "Models in search of a brain." Cognitive, Affective, & Behavioral Neuroscience 7, no. 2 (June 1, 2007): 90–108. http://dx.doi.org/10.3758/cabn.7.2.90.

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40

SPEAR, LINDA PATIA. "Adolescent Brain Development and Animal Models." Annals of the New York Academy of Sciences 1021, no. 1 (June 2004): 23–26. http://dx.doi.org/10.1196/annals.1308.002.

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41

Piotrowska, Monika. "Human Brain Surrogates: Models or Distortions?" American Journal of Bioethics 21, no. 1 (December 29, 2020): 66–68. http://dx.doi.org/10.1080/15265161.2020.1845867.

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42

Koh, Seong-Eun. "Animal Models of Traumatic Brain Injury." Brain & Neurorehabilitation 4, no. 1 (2011): 12. http://dx.doi.org/10.12786/bn.2011.4.1.12.

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43

RAJ, RAHUL. "Prognostic models in traumatic brain injury." Acta Anaesthesiologica Scandinavica 59, no. 5 (March 3, 2015): 679–80. http://dx.doi.org/10.1111/aas.12496.

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44

Petraglia, Anthony L., Matthew L. Dashnaw, Ryan C. Turner, and Julian E. Bailes. "Models of Mild Traumatic Brain Injury." Neurosurgery 75 (October 2014): S34—S49. http://dx.doi.org/10.1227/neu.0000000000000472.

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45

Freeman, Walter J. "Neurodynamic Models of Brain in Psychiatry." Neuropsychopharmacology 28, S1 (June 24, 2003): S54—S63. http://dx.doi.org/10.1038/sj.npp.1300147.

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46

Xiong, Ye, Asim Mahmood, and Michael Chopp. "Animal models of traumatic brain injury." Nature Reviews Neuroscience 14, no. 2 (January 18, 2013): 128–42. http://dx.doi.org/10.1038/nrn3407.

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47

Yingxu Wang and Ying Wang. "Cognitive informatics models of the brain." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 2 (March 2006): 203–7. http://dx.doi.org/10.1109/tsmcc.2006.871151.

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48

Friston, Karl J. "Models of Brain Function in Neuroimaging." Annual Review of Psychology 56, no. 1 (February 2005): 57–87. http://dx.doi.org/10.1146/annurev.psych.56.091103.070311.

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49

Friston, Karl J., and Cathy J. Price. "Generative models, brain function and neuroimaging." Scandinavian Journal of Psychology 42, no. 3 (July 2001): 167–77. http://dx.doi.org/10.1111/1467-9450.00228.

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50

Kogut, Paul, Jonathan Darvill, David Rosenbluth, and David Morgenthaler. "Top Down Bottom up Brain Models." Procedia Computer Science 41 (2014): 69–74. http://dx.doi.org/10.1016/j.procs.2014.11.086.

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