Academic literature on the topic 'Cerebellum model'
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Journal articles on the topic "Cerebellum model"
Kotani, Osamu, Tadaki Suzuki, Masaru Yokoyama, Naoko Iwata-Yoshikawa, Noriko Nakajima, Hironori Sato, Hideki Hasegawa, Fumihiro Taguchi, Hiroyuki Shimizu, and Noriyo Nagata. "Intracerebral Inoculation of Mouse-Passaged Saffold Virus Type 3 Affects Cerebellar Development in Neonatal Mice." Journal of Virology 90, no. 21 (August 31, 2016): 10007–21. http://dx.doi.org/10.1128/jvi.00864-16.
Full textKiffmeyer, Elizabeth A., Jameson A. Cosgrove, Jenna K. Siganos, Heidi E. Bien, Jade E. Vipond, Karisa R. Vogt, and Alexander D. Kloth. "Deficits in Cerebellum-Dependent Learning and Cerebellar Morphology in Male and Female BTBR Autism Model Mice." NeuroSci 3, no. 4 (November 9, 2022): 624–44. http://dx.doi.org/10.3390/neurosci3040045.
Full textGeminiani, Alice, Claudia Casellato, Alberto Antonietti, Egidio D’Angelo, and Alessandra Pedrocchi. "A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies." International Journal of Neural Systems 28, no. 05 (April 19, 2018): 1750017. http://dx.doi.org/10.1142/s0129065717500174.
Full textPollok, Bettina, Joachim Gross, Daniel Kamp, and Alfons Schnitzler. "Evidence for Anticipatory Motor Control within a Cerebello-Diencephalic-Parietal Network." Journal of Cognitive Neuroscience 20, no. 5 (May 2008): 828–40. http://dx.doi.org/10.1162/jocn.2008.20506.
Full textOmotoso, Gabriel Olaiya, Leviticus Oghenevurinrin Arietarhire, Ileje Inelo Ukwubile, and Ismail Temitayo Gbadamosi. "The Protective Effect of Kolaviron on Molecular, Cellular, and Behavioral Characterization of Cerebellum in the Rat Model of Demyelinating Diseases." Basic and Clinical Neuroscience Journal 11, no. 5 (September 1, 2020): 609–18. http://dx.doi.org/10.32598/bcn.9.10.300.
Full textKim, Jusik, Keeeun Kim, Jung-soon Mo, and Youngsoo Lee. "Atm deficiency in the DNA polymerase β null cerebellum results in cerebellar ataxia and Itpr1 reduction associated with alteration of cytosine methylation." Nucleic Acids Research 48, no. 7 (March 3, 2020): 3678–91. http://dx.doi.org/10.1093/nar/gkaa140.
Full textKnolle, Franziska, Erich Schröger, Pamela Baess, and Sonja A. Kotz. "The Cerebellum Generates Motor-to-Auditory Predictions: ERP Lesion Evidence." Journal of Cognitive Neuroscience 24, no. 3 (March 2012): 698–706. http://dx.doi.org/10.1162/jocn_a_00167.
Full textLiu, Qi, Chang Liu, Yu Chen, and Yumei Zhang. "Cognitive Dysfunction following Cerebellar Stroke: Insights Gained from Neuropsychological and Neuroimaging Research." Neural Plasticity 2022 (April 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/3148739.
Full textKurtaj, Lavdim, Vjosa Shatri, and Ilir Limani. "Cerebellar Model Controller with new Model of Granule Cell-golgi Cell Building Blocks and Two-phase Learning Acquires Multitude of Generalization Capabilities in Controlling Robot Joint without Exponential Growth in Complexity." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4292. http://dx.doi.org/10.11591/ijece.v8i6.pp4292-4309.
Full textShiba, Kazuhiro, Takashi Torashima, Hirokazu Hirai, Kazuma Ogawa, Nasima Akhter, Kenichi Nakajima, Seigo Kinuya, and Hirofumi Mori. "Potential Usefulness of D2R Reporter Gene Imaging by IBF as Gene Therapy Monitoring for Cerebellar Neurodegenerative Diseases." Journal of Cerebral Blood Flow & Metabolism 29, no. 2 (November 12, 2008): 434–40. http://dx.doi.org/10.1038/jcbfm.2008.137.
Full textDissertations / Theses on the topic "Cerebellum model"
Gavigan, Thomas. "VOLUMETRIC GROWTH MODEL OF HUMAN MEDULLOBLASTOMA IN THE NUDE MOUSE CEREBELLUM." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/133.
Full textSenatore, Rosa. "The role of basal ganglia and cerebellum in motor learning. A computational model." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/373.
Full textOur research activity investigates the computational processes underlying the execution of complex sequences of movements and aims at understanding how different levels of the nervous system interact and contribute to the gradual improvement of motor performance during learning. Many research areas, from neuroscience to engineering, investigate, from different perspectives and for diverse purposes, the processes that allow humans to efficiently perform skilled movements. From a biological point of view, the execution of voluntary movements requires the interaction between nervous and musculoskeletal systems, involving several areas, from the higher cortical centers to motor circuits in the spinal cord. Understanding these interactions could provide important insights for many research fields, from machine learning to medicine, from the design of robotic limbs to the development of new treatments for movement disorders, such as Parkinson’s disease. This goal could be achieved by finding an answer to the following questions: · How does the central nervous system control and coordinate natural voluntary movements? · Which brain areas are involved in learning a new motor skill? What are the changes that happen in these neural structures? What are the aspects of the movement memorized? · Which is the process that allows people to perform a skilled task, such as playing an instrument, being apparently unaware of the movements they are performing? · What happen when a neurodegenerative disease affects the brain areas involved in executing movements? These questions have been addressed from different perspectives and levels of analysis, from the exploration of the anatomical structure of the neural systems thought to be involved in motor learning (such as the basal ganglia, cerebellum and hippocampus) to the investigation of their neural interaction; from the analysis of the activation of these systems in executing a motor task to the specific activation of a single or a small group of neurons within them. In seeking to understand all the breadth and facets of motor learning, many researchers have used different approaches and methods, such as genetic analysis, neuroimaging techniques (such as fMRI, PET and EEG), animal models and clinical treatments (e.g. drugs administration and brain stimulation). These studies have provided a large body of knowledge that has led to several theories related to the role of the central nervous system in controlling and learning simple and complex movements. These theories envisage the interaction among multiple brain regions, whose cooperation leads to the execution of skilled movements. How can we test these interactions for the purpose of evaluating a theory? Our answer to this question is investigating these interactions through computational models, which provide a valuable complement to the experimental brain research, especially in evaluating the interactions within and among multiple neural systems. Based on these concepts arises our research, which addresses the questions previously pointed out and aims at understanding the computational processes performed by two neural circuits, the Basal Ganglia and Cerebellum, in motor learning. We propose a new hypothesis about the neural processes occurring during acquisition and retention of novel motor skills. According to our hypothesis, a sequence of movements is stored in the nervous system in the form of a spatial sequence of points (composing the trajectory plan associated to the motor sequence) and a sequence of motor commands. We propose that learning novel motor skills requires two phases, in which two different processes take place. Early in learning, when movements are slower, less accurate, and attention demanding, the motor sequence is performed by converting the sequence of target points into the appropriate sequence of motor commands. During this phase, the trajectory plan is acquired and the movements rely on the information provided by the visuo-proprioceptive feedback, which allows to correct the sequence of movements so that the actual trajectory plan corresponds to the desired one and the lowest energy is spent by the muscular subsystem involved. During the late learning phase, when the sequence of movements is performed faster and automatically, with little or no cognitive resources needed to complete it, and is characterized by anticipatory movements, the sequence of motor commands is acquired and thus, the sequence of movements comes to be executed as a single behavior. We suggest that the Basal Ganglia and Cerebellum are involved in learning novel motor sequences, although their role is crucial in different stages of learning. Accordingly, we propose a neural scheme for procedural motor learning, comprising the basal ganglia, cerebellum and cortex, which envisages that the basal ganglia, interacting with the cortex, select the sequence of target points to reach (composing the trajectory plan), whereas the cerebellum, interacting with the cortex, is responsible for converting the trajectory plan into the appropriate sequence of motor commands. Consequently, we suggest that early in learning, task performance is more dependent on the procedural knowledge maintained by the cortex-basal ganglia system, while after a long-term practice, when the sequence of motor commands is acquired within the cerebellum, task performance is more dependent on the motor command sequence maintained by the cortexcerebellar system. We tested the neural scheme (and the hypothesis behind it) through a computational model that incorporates the key anatomical, physiological and biological features of these brain areas in an integrated functional network. Analyzing the behavior of the network in learning novel motor tasks and executing well-known motor tasks, both in terms of the neural activations and motor response provided, we found that the results obtained fit those reported by many neuroimaging and experimental studies presented in the literature. We also carried out further experiments, simulating neurodegenerative disorders (Parkinson's and Huntington disease, which affect the basal ganglia) and cerebellar damages. Results obtained by these experiments validates the proposed hypothesis, showing that the basal ganglia play a key role during the early stage of learning, whereas the cerebellum is crucial for motor skill retention. Our model provides some insights about the learning mechanisms occurring within the cerebellum and gains further understanding of the functional dynamics of information processing within the basal ganglia and cerebellum in normal as well as in diseased brains. Therefore the model provides novel predictions about the role of basal ganglia and cerebellum in motor learning, motivating further investigations of their interactions. [edited by author]
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Babenko, Olena Mykolayivna, and University of Lethbridge Faculty of Arts and Science. "The molecular mechanisms underlying epigenetics of the stress response in the cerebellum in a rat model." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Biological Sciences, c2010, 2010. http://hdl.handle.net/10133/2604.
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Chintawar, Satyan. "Neural precursor cells: interaction with blood-brain barrier and neuroprotective effect in an animal model of cerebellar degeneration." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210202.
Full textIn a brain stem cell niche, NSCs reside in a complex cellular and extracellular microenvironment comprising their own progeny, ependymal cells, numerous blood vessels and various extracellular matrix molecules. Recently, it was reported that blood vessel ECs-NSCs crosstalk plays an important role in tissue homeostasis. Bloodstream offers a natural delivery vehicle especially in case of diffuse neurodegenerative diseases which require widespread distribution of exogenous cells. As NSCs are confronted with blood-brain barrier endothelial cells (BBB-ECs) before they can enter into brain parenchyma, we investigated their interaction using primary cultures in an in vitro BBB model. We isolated human fetal neural precursor cells (hfNPCs) from aborted fetal brain tissues and expanded in vitro. We showed that in an in vitro model, human BBB endothelium induces the rapid differentiation of hfNPCs and allows them to cross the endothelial monolayer, with the differentiated progeny remaining in close contact with endothelial cells. These results are not reproduced when using a non-BBB endothelium and are partly dependent on the cytokine MCP1. Our data suggest that, in the presence of attractive signals released by a damaged brain, intravascularly administered NPCs can move across an intact BBB endothelium and differentiate in its vicinity. Overall, our findings have implications for the development of cellular therapies for cerebellar degenerative diseases and understanding of the brain stem cell niche.
Doctorat en Sciences biomédicales et pharmaceutiques
info:eu-repo/semantics/nonPublished
Takagishi, Yoshiko, 芳子 高岸, and Yoshiharu Murata. "Myosin Va mutation in rats is an animal model for the human hereditary neurological disease, Griscelli syndrome type 1." New York Academy of Sciences, 2006. http://hdl.handle.net/2237/10947.
Full textHecker, David [Verfasser]. "Migration of interneuronal precursor cells in the developing cerebellum of mice : model-based cell tracking and simulation / David Hecker." Bonn : Universitäts- und Landesbibliothek Bonn, 2010. http://d-nb.info/1016155654/34.
Full textKlein, de Licona Hannah Washington. "Congenital LCMV virus: mechanism of brain disease in a rat model of congenital viral infection." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/531.
Full textBalastik, Martin. "Trim2 mutant mice as a model for cerebellar ataxia." Doctoral thesis, [S.l.] : [s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=975117025.
Full textMARSHALL, CRAIG ANTHONY. "QUANTITATIVE MEASUREMENT OF THE EXPRESSION OF TWO GENES IN THE CORETX AND CEREBELLUM OF A MOUSE MODEL OF JUVENILE ALZHEIMER’S." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613283.
Full textParnell, Scott E., Jayanth Ramadoss, Michael D. Delp, Michael W. Ramsey, Wei-Jung A. Chen, James R. West, and Timothy A. Cudd. "Chronic Ethanol Increases Fetal Cerebral Blood Flow Specific to the Ethanol-Sensitive Cerebellum Under Normoxaemic, Hypercapnic and Acidaemic Conditions: Ovine Model." Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etsu-works/4134.
Full textBooks on the topic "Cerebellum model"
David, Rogers, and Research Institute for Advanced Computer Science (U.S.), eds. Temporal learning in the cerebellum: The MicroCircuit model. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.
Find full textEskandarian, Azim. A reference model dynamics-CMAC algorithm for simulation and control of robotic manipulators. [S.l.]: George Washington University, 1991.
Find full textNeural transplantation in cerebellar ataxia. Austin, Tex: Landes, 1997.
Find full textRandolph, Raugh Michael, Ames Research Center, Research Institute for Advanced Computer Science (U.S.), and Compcon (34th : 1989 : San Francisco, Calif.), eds. Cerebellar models of associative memory: Three papers from IEEE COMPCON Spring '89. [Moffettt Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Find full textEly, Budding Deborah, ed. Subcortical structures and cognition: Implications for neuropsychological assessment. New York: Springer, 2009.
Find full text1946-, Vaina Lucia, ed. From the retina to the neocortex: Selected papers of David Marr. Boston: Birkhäuser, 1991.
Find full textMason, Peggy. Cerebellum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190237493.003.0024.
Full textSoong, Bing-wen. Trials for Cerebellar Ataxias: From Cellular Models to Human Therapies. Springer International Publishing AG, 2023.
Find full textBerman, Frederick W. Development and characterization of a model of glutamate and domoate toxicity in cultured rat cerebellar granule neurons. 1997.
Find full textKoziol, Leonard F., and Deborah Ely Budding. Subcortical Structures and Cognition: Implications for Neuropsychological Assessment. Springer New York, 2010.
Find full textBook chapters on the topic "Cerebellum model"
Koziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "The Cerebellum." In ADHD as a Model of Brain-Behavior Relationships, 51–53. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_18.
Full textTravis, Bryan J. "A Computational Model of the Cerebellum." In Analysis and Modeling of Neural Systems, 131–37. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4010-6_14.
Full textHouk, J. C. "Model of the Cerebellum as an Array Of Adjustable Pattern Generators." In Cerebellum and Neuronal Plasticity, 249–60. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-0965-9_16.
Full textKoziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "The Modular Organization of the Cerebellum." In ADHD as a Model of Brain-Behavior Relationships, 55. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_19.
Full textZhang, Shaobai, and Qun Chen. "Study over Cerebellum Prediction Model During Hand Tracking." In Communications in Computer and Information Science, 159–67. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3966-9_17.
Full textAntonietti, Alberto, Claudia Casellato, Egidio D’Angelo, and Alessandra Pedrocchi. "Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout." In Lecture Notes in Computer Science, 35–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_3.
Full textSotelo, Constantino. "Cerebellar Transplantation: A Potential Model to Study Repair and Development of Neurons and Circuits in the Cerebellum." In Development of the Cerebellum from Molecular Aspects to Diseases, 465–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59749-2_22.
Full textPessac, Bernard, and Françoise Alliot. "Do Mouse Cerebellum Astrocytes Play a Role in Neuronal Survival and Differentiation?" In Model Systems of Development and Aging of the Nervous System, 201–8. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-2037-1_14.
Full textSotelo, Constantino. "Cerebellar Transplantation: A Potential Model to Study Repair and Development of Neurons and Circuits in the Cerebellum." In Contemporary Clinical Neuroscience, 605–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23104-9_26.
Full textShim, Vui Ann, Chris Stephen Naveen Ranjit, Bo Tian, and Huajin Tang. "A Simplified Cerebellum-Based Model for Motor Control in Brain Based Devices." In Neural Information Processing, 520–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42054-2_65.
Full textConference papers on the topic "Cerebellum model"
Kawato, M., and H. Gomi. "Model of four regions of the cerebellum." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170436.
Full text"Brain-inspired Sensorimotor Robotic Platform - Learning in Cerebellum-driven Movement Tasks through a Cerebellar Realistic Model." In Special Session on Challenges in Neuroengineering. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004659305680573.
Full textZhang Shao-bai, Ruan Xiao-gang, and Cheng Xiefeng. "A new constructing method of cerebellum model applying to DIVA model." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192809.
Full textZhou, Chenye, and Shaobai Zhang. "Research on timing function of cerebellum in DIVA model." In International Conference on Communication Technology. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icct130381.
Full textMathew, Seema, Parimal Giri, and Manjusha Agwan. "A Simulink Implementation of the Cerebellum Model Articulation Controller." In 2010 Second International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT). IEEE, 2010. http://dx.doi.org/10.1109/act.2010.47.
Full textPresannan, Anandhu, Arathi Rajendran, Bipin Nair, and Shyam Diwakar. "Reproducing the Firing Properties of a Cerebellum Deep Cerebellar Nucleus with a Multi-Compartmental Morphologically Realistic Biophysical Model." In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018. http://dx.doi.org/10.1109/icacci.2018.8554491.
Full textLuo, Junwen, Graeme Coapes, Patrick Degenaar, Tadashi Yamazaki, Terrence Mak, and Chung Tin. "A real-time silicon cerebellum spiking neural model based on FPGA." In 2014 International Symposium on Integrated Circuits (ISIC). IEEE, 2014. http://dx.doi.org/10.1109/isicir.2014.7029586.
Full textYoosef, Afila, Harilal Parasuram, Chaitanya Medini, Sergio Solinas, Egidio D'Angelo, Bipin Nair, and Shyam Diwakar. "Parallelization of a Computational Model of a Biophysical Neuronal Circuitry of Rat Cerebellum." In the 2014 International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2660859.2660962.
Full textGeminiani, Alice, Aurimas Mockevicius, Egidio D'Angelo, and Claudia Casellato. "Cerebellum involvement in dystonia: insights from a spiking neural network model during associative learning." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871205.
Full textReyes López, Misael, Fernando Arámbula Cosío, Boris Escalante Ramírez, and Jimena Olveres Montiel. "Shape model and Hermite features for the segmentation of the cerebellum in fetal ultrasound." In 14th International Symposium on Medical Information Processing and Analysis, edited by Eduardo Romero, Natasha Lepore, and Jorge Brieva. SPIE, 2018. http://dx.doi.org/10.1117/12.2511411.
Full textReports on the topic "Cerebellum model"
Gambello, Michael. Behavioral Analysis and Rescue of a Novel Cerebellar Mouse Model of Tuberous Sclerosis Complex. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada566012.
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