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Journal articles on the topic 'Computationnal Neuroscience'

Author: Grafiati

Published: 7 July 2024

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1

Herrmann-Pillath, Carsten. "From dual systems to dual function: rethinking methodological foundations of behavioural economics." Economics and Philosophy 35, no. 3 (2019): 403–22. http://dx.doi.org/10.1017/s0266267118000378.

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AbstractBuilding on an overview of dual systems theories in behavioural economics, the paper presents a methodological assessment in terms of the mechanistic explanations framework that has gained prominence in philosophy of the neurosciences. I conclude that they fail to meet the standards of causal explanations and I suggest an alternative ‘dual functions’ view based on Marr’s methodology of computational neuroscience. Recent psychological and neuroscience research undermines the case for a categorization of brain processes in terms of properties such as relative speed. I defend an interpretation of dualities as functional, without assigning them to specific neurophysiological structures.

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2

Cao, Jinde, Qingshan Liu, Sabri Arik, Jianlong Qiu, Haijun Jiang, and Ahmed Elaiw. "Computational Neuroscience." Computational and Mathematical Methods in Medicine 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/120280.

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3

Sejnowski, T., C. Koch, and P. Churchland. "Computational neuroscience." Science 241, no. 4871 (1988): 1299–306. http://dx.doi.org/10.1126/science.3045969.

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4

Sejnowski, Terrence J. "Computational neuroscience." Behavioral and Brain Sciences 9, no. 1 (1986): 104–5. http://dx.doi.org/10.1017/s0140525x00021713.

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5

Moore, John W. "Computational Neuroscience." Contemporary Psychology: A Journal of Reviews 38, no. 2 (1993): 137–39. http://dx.doi.org/10.1037/033019.

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6

Ringo, J. L. "Computational Neuroscience." Archives of Neurology 48, no. 2 (1991): 130. http://dx.doi.org/10.1001/archneur.1991.00530140018008.

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7

Kriegeskorte, Nikolaus, and Pamela K. Douglas. "Cognitive computational neuroscience." Nature Neuroscience 21, no. 9 (2018): 1148–60. http://dx.doi.org/10.1038/s41593-018-0210-5.

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8

Cecchi, Guillermo A., and James Kozloski. "Preface: Computational neuroscience." IBM Journal of Research and Development 61, no. 2/3 (2017): 0:1–0:4. http://dx.doi.org/10.1147/jrd.2017.2690118.

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9

Popovych, Oleksandr, Peter Tass, and Christian Hauptmann. "Desynchronization (computational neuroscience)." Scholarpedia 6, no. 10 (2011): 1352. http://dx.doi.org/10.4249/scholarpedia.1352.

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10

Érdi, Péter. "Teaching computational neuroscience." Cognitive Neurodynamics 9, no. 5 (2015): 479–85. http://dx.doi.org/10.1007/s11571-015-9340-6.

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11

Becker, Suzanna, and Nathaniel D. Daw. "Computational cognitive neuroscience." Brain Research 1299 (November 2009): 1–2. http://dx.doi.org/10.1016/j.brainres.2009.09.114.

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12

Vibert, Jean-Francois, Khashayar Pakdaman, Eric Boussard, and Evyatar Av-Ron. "Computational neuroscience and neurology." Nature Medicine 1, no. 12 (1995): 1247–48. http://dx.doi.org/10.1038/nm1295-1247.

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13

Destexhe, Alain, and Vincenzo Crunelli. "Methods for computational neuroscience." Journal of Neuroscience Methods 169, no. 2 (2008): 269–70. http://dx.doi.org/10.1016/j.jneumeth.2008.01.025.

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14

Rothwell, John C., R. Shadmehr, and B. Yates. "Neurophysiology and computational neuroscience." Experimental Brain Research 200, no. 3-4 (2010): 189–91. http://dx.doi.org/10.1007/s00221-010-2164-z.

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15

Piccinini, Gualtiero, and Oron Shagrir. "Foundations of computational neuroscience." Current Opinion in Neurobiology 25 (April 2014): 25–30. http://dx.doi.org/10.1016/j.conb.2013.10.005.

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16

Fairhall, Adrienne, and Christian Machens. "Editorial overview: Computational neuroscience." Current Opinion in Neurobiology 46 (October 2017): A1—A5. http://dx.doi.org/10.1016/j.conb.2017.09.009.

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17

Doiron, Brent, and Máté Lengyel. "Editorial overview: Computational neuroscience." Current Opinion in Neurobiology 58 (October 2019): iii—vii. http://dx.doi.org/10.1016/j.conb.2019.09.015.

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18

Wu, Si, and PeiJi Liang. "Computational neuroscience in China." Science China Life Sciences 53, no. 3 (2010): 385–97. http://dx.doi.org/10.1007/s11427-010-0063-y.

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19

Kawato, M. "Computational neuroscience for psychiatry." International Journal of Psychophysiology 131 (October 2018): S5—S6. http://dx.doi.org/10.1016/j.ijpsycho.2018.07.019.

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20

Almeida, Antônio-Carlos Guimarães de, Antônio Márcio Rodrigues, and Antonio Fernando Catelli Infantosi. "Computational neuroscience in perspective." Revista Brasileira de Engenharia Biomédica 30, no. 3 (2014): 205–6. http://dx.doi.org/10.1590/1517-3151.3003.

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21

Izhikevich, Eugene. "Encyclopedia of computational neuroscience." Scholarpedia 6, no. 12 (2011): 1286. http://dx.doi.org/10.4249/scholarpedia.1286.

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22

Piccinini, Gualtiero. "Computational explanation in neuroscience." Synthese 153, no. 3 (2006): 343–53. http://dx.doi.org/10.1007/s11229-006-9096-y.

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23

Izawa, Jun. "Computational Neurorehabilitation：Robotic Rehabilitation Aided by Computational Neuroscience." Japanese Journal of Rehabilitation Medicine 57, no. 1 (2020): 56–63. http://dx.doi.org/10.2490/jjrmc.57.56.

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24

Sadnicka, Anna, Lydia Mardell, and Sven Bestmann. "Computational neuroscience with global accessibility." Lancet Neurology 20, no. 4 (2021): 257–58. http://dx.doi.org/10.1016/s1474-4422(21)00074-0.

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25

Yilancioglu, Kaan. "Systems Biology and Computational Neuroscience." Journal of Neurobehavioral Sciences 1, no. 3 (2014): 99. http://dx.doi.org/10.5455/jnbs.1415621109.

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26

Eglash, Ron. "Hip Hop as Computational Neuroscience." International Journal of Information, Diversity, & Inclusion (IJIDI) 6, no. 1/2 (2022): 11–30. http://dx.doi.org/10.33137/ijidi.v6i1.37127.

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Long before the internet provided us with a networked digital system, music exchanges had created a global networked analog system, built of recordings, radio broadcasts, and live performance. The features that allowed some audio formations to go viral, while others failed, fall at the intersection of three domains: access, culture, and cognition. We know how the explosive growth of the hip hop recording industry addressed the access problem, and how hip hop lyrics addressed cultural needs. But why does hip hop make your ass shake? This essay proposes that hip hop artists were creating an innovation in brain-to-brain connectivity. That is to say, there are deep parts of the limbic system that had not previously been connected to linguistic centers in the combination of neural and social pathways that hip hop facilitated. This research is not an argument for using computational neuroscience to analyze hip hop. Rather, it is asking what hip hop artists accomplished as the street version of computational neuroscientists; and, how they strategically deployed Black music traditions to rewire the world’s global rhythmic nervous system for new cognitive, cultural, and political alignments and sensibilities.

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27

Tatler, Benjamin W. "Reviews: Computational Neuroscience of Vision." Perception 31, no. 10 (2002): 1283–84. http://dx.doi.org/10.1068/p3110rvw.

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28

Churchland, Patricia S., and Terrence J. Sejnowski. "Blending computational and experimental neuroscience." Nature Reviews Neuroscience 17, no. 11 (2016): 667–68. http://dx.doi.org/10.1038/nrn.2016.114.

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29

Wolpert, Daniel M., and Zoubin Ghahramani. "Computational principles of movement neuroscience." Nature Neuroscience 3, S11 (2000): 1212–17. http://dx.doi.org/10.1038/81497.

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30

Graham, Lyle J. "ModelDB, Neuroinformatics, and Computational Neuroscience." Neuroinformatics 1, no. 1 (2003): 141–44. http://dx.doi.org/10.1385/ni:1:1:141.

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31

Zhang, Jing, Tianming Liu, and Gopikrishna Deshpande. "Probabilistic Methods in Computational Neuroscience." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 2 (2018): 535–36. http://dx.doi.org/10.1109/tcbb.2018.2804399.

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32

Wennekers, Thomas. "Special Issue on Computational Neuroscience." Journal of Neuroscience Methods 210, no. 1 (2012): 1–2. http://dx.doi.org/10.1016/j.jneumeth.2012.08.008.

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33

Bancroft, Tyler D. "Ethical Aspects of Computational Neuroscience." Neuroethics 6, no. 2 (2012): 415–18. http://dx.doi.org/10.1007/s12152-012-9163-7.

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34

Burton, Esme. "E.L. Schwartz (ed.), Computational neuroscience." Biological Psychology 31, no. 3 (1990): 303–5. http://dx.doi.org/10.1016/0301-0511(90)90069-9.

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35

Morse, Thomas. "Model sharing in computational neuroscience." Scholarpedia 2, no. 4 (2007): 3036. http://dx.doi.org/10.4249/scholarpedia.3036.

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36

Teeters, Jeffrey L., Kenneth D. Harris, K. Jarrod Millman, Bruno A. Olshausen, and Friedrich T. Sommer. "Data Sharing for Computational Neuroscience." Neuroinformatics 6, no. 1 (2008): 47–55. http://dx.doi.org/10.1007/s12021-008-9009-y.

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37

De Schutter, Erik. "Deep Learning and Computational Neuroscience." Neuroinformatics 16, no. 1 (2018): 1–2. http://dx.doi.org/10.1007/s12021-018-9360-6.

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38

Kawato, Mitsuo. "Computational neuroscience of early vision." Neuroscience Research Supplements 15 (January 1990): S7. http://dx.doi.org/10.1016/0921-8696(90)90059-c.

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39

Kawato, Mitsuo. "Computational neuroscience of early vision." Neuroscience Research Supplements 11 (January 1990): S7. http://dx.doi.org/10.1016/0921-8696(90)90482-i.

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40

Schmidt, Louis A., and Jay Schulkin. "Toward a Computational Affective Neuroscience." Brain and Cognition 42, no. 1 (2000): 95–98. http://dx.doi.org/10.1006/brcg.1999.1172.

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41

Jiang, Weihang. "Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience." Applied and Computational Engineering 4, no. 1 (2023): 473–78. http://dx.doi.org/10.54254/2755-2721/4/2023308.

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High-performance machine learning algorithms have always been one of the concerns of many researchers. Since its birth, machine learning has been a product of multidisciplinary integration. Especially in the field of neuroscience, models from related fields continue to inspire the development of neural networks and deepen people's understanding of neural networks. The mathematical and quantitative modeling approach to research brought about by machine learning is also feeding into the development of neuroscience. One of the emerging products of this is computational neuroscience. Computational neuroscience has been pushing the boundaries of models of brain function in recent years, and just as early studies of visual hierarchy influenced neural networks, computational neuroscience has great potential to lead to higher performance machine learning algorithms, particularly in the development of deep learning algorithms with strong links to neuroscience. In this paper, it first reviews the help and achievements of machine learning for neuroscience in recent years specially in fMRI image recognition and look at the possibilities for the future development of neural networks due to the recent development of the computational neuroscience in psychiatry of the temporal difference model for dopamine and serotonin.

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42

Anatolou, Dimitra, and Marios G. Krokidis. "Computational analysis of peripheral blood RNA sequencing data unravels disrupted immune patterns in Alzheimer's disease." AIMS Neuroscience 11, no. 2 (2024): 103–17. http://dx.doi.org/10.3934/neuroscience.2024007.

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<abstract> <p>The central nervous system (CNS) and the immune system collectively coordinate cellular functionalities, sharing common developmental mechanisms. Immunity-related molecules exert an influence on brain development, challenging the conventional view of the brain as immune-privileged. Chronic inflammation emerges as a key player in the pathophysiology of Alzheimer's disease (AD), with increased stress contributing to the disease progression and potentially exacerbating existing symptoms. In this study, the most significant gene signatures from selected RNA-sequencing (RNA-seq) data from AD patients and healthy individuals were obtained and a functional analysis and biological interpretation was conducted, including network and pathway enrichment analysis. Important evidence was reported, such as enrichment in immune system responses and antigen processes, as well as positive regulation of T-cell mediated cytotoxicity and endogenous and exogenous peptide antigen, thus indicating neuroinflammation and immune response participation in disease progression. These findings suggest a disturbance in the immune infiltration of the peripheral immune environment, providing new challenges to explore key biological processes from a molecular perspective that strongly participate in AD development.</p> </abstract>

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43

KAWATO, Mitsuo. "Artificial Intelligence (AI) and Computational Neuroscience." TRENDS IN THE SCIENCES 21, no. 4 (2016): 4_70–4_73. http://dx.doi.org/10.5363/tits.21.4_70.

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44

Fernandez Leon, Jose A., and Gerardo G. Acosta. "Challenges for neuroscience-based computational intelligence." International Journal of Computational Intelligence Studies 10, no. 4 (2021): 232. http://dx.doi.org/10.1504/ijcistudies.2021.10044488.

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45

Leon, Jose A. Fernandez, and Gerardo G. Acosta. "Challenges for neuroscience-based computational intelligence." International Journal of Computational Intelligence Studies 10, no. 4 (2021): 232. http://dx.doi.org/10.1504/ijcistudies.2021.120507.

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46

Rolls, E. T. "David Marr's Vision: floreat computational neuroscience." Brain 134, no. 3 (2011): 913–16. http://dx.doi.org/10.1093/brain/awr013.

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47

Kass, Robert E., Shun-Ichi Amari, Kensuke Arai, et al. "Computational Neuroscience: Mathematical and Statistical Perspectives." Annual Review of Statistics and Its Application 5, no. 1 (2018): 183–214. http://dx.doi.org/10.1146/annurev-statistics-041715-033733.

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48

Borg-Graham, Lyle. "Facilitating the science in computational neuroscience." Nature Neuroscience 3, S11 (2000): 1191. http://dx.doi.org/10.1038/81467.

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49

Kaitwanidvilai, Somyot, Uma Seeboonruang, Hisayuki Aoyama, and Khemraj Emrith. "Computational Intelligence and Neuroscience in Neurorobotics." Computational Intelligence and Neuroscience 2019 (July 1, 2019): 1–2. http://dx.doi.org/10.1155/2019/6506802.

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50

Blackwell, Kim T. "A New Era in Computational Neuroscience." Neuroinformatics 3, no. 2 (2005): 163–66. http://dx.doi.org/10.1385/ni:3:2:163.

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