https://m2.mtmt.hu/ hun 2026-09-23 04:23 JournalArticle 33199928 VALIDATED true 0 false 2026-05-22T06:42:57.429+0000 2026-05-14T09:05:22.017+0000 2022-10-30T13:50:46.919+0000 true 10084426 Berki Ádám József /api/author/10084426 Author Berki Ádám József (Neurológia, idegtudományok) true 2026-05-14T09:05:22.290+0000 2026-05-14T09:05:22.290+0000 2022-11-17T08:22:08.650+0000 true 10080205 Szalóky-Siki Ágnes /api/admin/10080205 Admin Szalóky-Siki Ágnes (SE_KK_Admin5_SZSA, admin) true true false true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType Szakcikk (Folyóiratcikk) 101 true 10000059 true 1 /api/category/1 Category Tudományos true 1 Végh, János true true /api/journal/30 REVIEWED ACTA BIOTHEORETICA 0001-5342 1572-8358 true false 30 false 30 true 0001-5342 1572-8358 Journal FOREIGN 70 4 26 26 25 2022 In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing. Funding 2020341 /api/funding/2020341 (136496) false true true true 2022 true true true false true false NONE 2026-05-22 false 4 0 4 1 1 4 4 2 4 4 4 1 1 1 3 0 4 1 4 1 0 4 4 Folyóiratcikk Journal Article 24 2 Könyvrészlet Chapter in Book 25 1 Könyv Book 23 1 Egyéb konferenciaközlemény Conference paper 31 0 Egyéb konferenciakötet Conference proceedings 32 0 Oltalmi formák Protection forms 26 0 Disszertáció Thesis 28 0 Egyéb Miscellaneous 29 0 Alkotás Achievement 22 0 Kutatási adat Research data 33 0 Folyóiratcikk Journal Article 24 0 0 0 Könyvrészlet Chapter in Book 25 0 0 0 Könyv Book 23 0 0 0 Egyéb konferenciaközlemény Conference paper 31 0 0 0 Egyéb konferenciakötet Conference proceedings 32 0 0 0 Oltalmi formák Protection forms 26 0 0 0 Disszertáció Thesis 28 0 0 0 Egyéb Miscellaneous 29 0 0 0 Alkotás Achievement 22 0 0 0 Kutatási adat Research data 33 0 0 0 2023 0 2 0 2 0 2024 0 0 0 0 0 2025 0 2 1 2 1 Q2 false false false false 2022-11-09T10:46:22.436+0000 Doktori Iskola (SE); Neurológiai Klinika (SE / AOK / K) 2 17 2 2 0 true Language 10002 /api/language/10002 Angol Angol English true 2 true PersonAuthorship 104079674 /api/authorship/104079674 Végh, János ✉ [Végh, János (Atomfizika), szerző] 1 0.5 true false true Author 10007792 /api/author/10007792 Végh János (Atomfizika) Végh János DUPLUM true 10007792 true Végh János true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 104079675 /api/authorship/104079675 Berki, Ádám József* ✉ [Berki, Ádám József (Neurológia, idegt...), szerző] Neurológiai Klinika (SE / AOK / K); Doktori Iskola (SE) 2 0.5 true false true Author 10084426 /api/author/10084426 Berki Ádám József (Neurológia, idegtudományok) Berki Ádám József true true Berki Ádám József true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PublicationIdentifier 21966013 /api/publicationidentifier/21966013 DOI: 10.1007/s10441-022-09450-6 PlainSource 6 /api/publicationsource/6 DOI PublicationSourceType 10001 /api/publicationsourcetype/10001 DOI true true true DOI DOI https://doi.org/@@@ true true 6 true IDENTICAL 10.1007/s10441-022-09450-6 https://doi.org/10.1007/s10441-022-09450-6 false true PublicationIdentifier 22022061 /api/publicationidentifier/22022061 WoS: 000873837400001 PlainSource 1 /api/publicationsource/1 WoS PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 000873837400001 https://www.webofscience.com/wos/woscc/full-record/000873837400001 true true PublicationIdentifier 22022012 /api/publicationidentifier/22022012 Scopus: 85140580130 PlainSource 3 /api/publicationsource/3 Scopus PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true Scopus Scopus http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-@@@ true true 3 true IDENTICAL 85140580130 http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85140580130 true true PublicationIdentifier 22022026 /api/publicationidentifier/22022026 PubMed: 36287247 PlainSource 17 /api/publicationsource/17 PubMed PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true PubMed PubMed http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=@@@&dopt=Abstract true true 17 true IDENTICAL 36287247 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=36287247&dopt=Abstract true true PublicationIdentifier 23475501 /api/publicationidentifier/23475501 Egyéb URL: https://www.preprints.org/manuscript/202103.0414/v1 PlainSource 40 /api/publicationsource/40 Egyéb URL PublicationSourceType 10006 /api/publicationsourcetype/10006 Link true true true Egyéb URL Other URL @@@ true true 40 true https://www.preprints.org/manuscript/202103.0414/v1 https://www.preprints.org/manuscript/202103.0414/v1 false true Classification 12162 /api/classification/12162 Neurológia true true SjrRating 11272763 /api/sjrrating/11272763 sjr:Q2 (2022) Scopus - Agricultural and Biological Sciences (miscellaneous) ACTA BIOTHEORETICA 0001-5342 1572-8358 166 0.49 journal RatingType 10002 /api/ratingtype/10002 sjr sjr true true ClassificationExternal 1101 /api/classificationexternal/1101 Scopus - Agricultural and Biological Sciences (miscellaneous) true 1101 true Q2 DIRECT true true Reference 35175869 /api/reference/35175869 1. Abbott A (2020) Documentary follows implosion of billion-euro brain project. Nature 588:215–216. https://doi.org/10.1038/d41586-020-03462-3, DOI: 10.1038/d41586-020-03462-3 1 10.1038/d41586-020-03462-3 false true Reference 35175870 /api/reference/35175870 2. Abraham I (2018) The case for rejecting the memristor as a fundamental circuit element. Nat Sci Rep 8:10972. https://doi.org/10.1038/s41598-018-29394-7, DOI: 10.1038/s41598-018-29394-7 2 10.1038/s41598-018-29394-7 false true Reference 35175871 /api/reference/35175871 3. Almeida RG, Lyons DA (2017) On myelinated axon plasticity and neuronal circuit formation and function. J Neurosci 37:10023–10034, DOI: 10.1523/JNEUROSCI.3185-16.2017 3 10.1523/JNEUROSCI.3185-16.2017 false true Reference 35175872 /api/reference/35175872 4. Amdahl GM (1967) Validity of the single processor approach to achieving large-scale computing capabilities 30:483–485 4 false true Reference 35175873 /api/reference/35175873 5. Antle MC, Silver R (2015) Orchestrating time: arrangements of the brain circadian clock. Trends Neurosci 28:145–151, DOI: 10.1016/j.tins.2005.01.003 5 10.1016/j.tins.2005.01.003 false true Reference 35175874 /api/reference/35175874 6. Arancibia-Cárcamo IL et al (2017) Node of Ranvier length as a potential regulator of myelinated axon conduction speed, DOI: 10.7554/eLife.23329.009 6 10.7554/eLife.23329.009 false true Reference 35175875 /api/reference/35175875 7. Asanovic K et al (2009) A view of the parallel computing landscape. Commun ACM 52(10):56–67, DOI: 10.1145/1562764.1562783 7 10.1145/1562764.1562783 false true Reference 35175876 /api/reference/35175876 8. Backus J (1978) Can programming languages be liberated from the von Neumann style? A functional style and its algebra of programs. Commun ACM 21:613–641, DOI: 10.1145/359576.359579 8 10.1145/359576.359579 false true Reference 35175877 /api/reference/35175877 9. Bengio E, Bacon P-L, Pineau J,Precu D (2016) Conditional computation in neural networks for faster models. arXiv.org/pdf/1511.06297 9 false true Reference 35175878 /api/reference/35175878 10. Benke TA, Lüthi A, Isaac JTR, Collingridge GL (1998) Modulation of AMPA receptor unitary conductance by synaptic activity. Nature 393:1629–1636, DOI: 10.1038/31709 10 10.1038/31709 false true Reference 35175879 /api/reference/35175879 11. Buzsáki G (2019) The brain from inside out, 1st edn. Oxford University Press, Oxford, DOI: 10.1093/oso/9780190905385.001.0001 11 10.1093/oso/9780190905385.001.0001 false true Reference 35175880 /api/reference/35175880 12. Buzsáki G, Wang X-J (2012) Mechanisms of gamma oscillations. Ann Rev Neurosci 3(4):191–1929. https://doi.org/10.1146/annurev-neuro-062111-150444, DOI: 10.1146/annurev-neuro-062111-150444 12 10.1146/annurev-neuro-062111-150444 false true Reference 35175881 /api/reference/35175881 13. Chicca E, Indiveri G (2020) A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems. Appl Phys Lett 116(12):120501. https://doi.org/10.1063/1.5142089, DOI: 10.1063/1.5142089 13 10.1063/1.5142089 false true Reference 35175882 /api/reference/35175882 14. D’Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial-temporal features extraction. J Netw Comput Appl 173:102890. https://doi.org/10.1016/j.jnca.2020.102890, DOI: 10.1016/j.jnca.2020.102890 14 10.1016/j.jnca.2020.102890 false true Reference 35175883 /api/reference/35175883 15. D’Angelo G, Rampone S (2014) Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications. BMC Bioinform 15, DOI: 10.1186/1471-2105-15-S5-S2 15 10.1186/1471-2105-15-S5-S2 false true Reference 35175884 /api/reference/35175884 16. Eckert JP, Mauchly JW (1945) Automatic high-speed computing: a progress report on the EDVAC. Tech. Rep. of Work under Contract No. W-670-ORD-4926, Supplement No 4, Moore School Library, University of Pennsylvania, Philadephia 16 false true Reference 35175885 /api/reference/35175885 17. Esmaeilzadeh H, Blem E, St. Amant R, Sankaralingam K, Burger D (2012) Dark silicon and the end of multicore scaling. IEEE Micro 32(3):122–134, DOI: 10.1109/MM.2012.17 17 10.1109/MM.2012.17 false true Reference 35175886 /api/reference/35175886 18. Ford M, Alexandrova O, Cossell L (2015) Node of Ranvier length as a potential regulator of myelinated axon conduction speed. Nat Commun 6:8073, DOI: 10.1038/ncomms9073 18 10.1038/ncomms9073 false true Reference 35175887 /api/reference/35175887 19. Godfrey MD, Hendry DF (1993) The computer as von Neumann planned it. IEEE Ann Hist Comput 15(1):11–21, DOI: 10.1109/85.194088 19 10.1109/85.194088 false true Reference 35175888 /api/reference/35175888 20. Grubl A, Billaudelle S, Cramer B, Karasenko V, Schemmel J (2020) Verification and design methods for the BrainScaleS neuromorphic hardware system. J Signal Process Syst 92:1277–1292. https://doi.org/10.1007/s11265-020-01558-7, DOI: 10.1007/s11265-020-01558-7 20 10.1007/s11265-020-01558-7 false true Reference 35175889 /api/reference/35175889 21. Hameed R et al (2010) Understanding sources of inefficiency in general-purpose chips. ISCA ’10, 37–47, DOI: 10.1145/1815961.1815968 21 10.1145/1815961.1815968 false true Reference 35175890 /api/reference/35175890 22. Hebb D (1949) The organization of behavior. Wiley and Sons, New York 22 false true Reference 35175891 /api/reference/35175891 23. Hutson M (2020) Core progress in AI has stalled in some fields. Science 368:6494. https://doi.org/10.1126/science.368.6494.927, DOI: 10.1126/science.368.6494.927 23 10.1126/science.368.6494.927 false true Reference 35175892 /api/reference/35175892 24. IEEE/Accellera. Systems initiative. http://www.accellera.org/downloads/standards/systemc (2017) 24 false true Reference 35175893 /api/reference/35175893 25. Kendall JD, Kumar S (2020) The building blocks of a brain-inspired computer. Appl Phys Rev 7:011305. https://doi.org/10.1063/1.5129306, DOI: 10.1063/1.5129306 25 10.1063/1.5129306 false true Reference 35175894 /api/reference/35175894 26. Keuper J, Pfreundt F-J (2016) Distributed training of deep neural networks: theoretical and practical limits of parallel scalability, pp 1469–1476 (IEEE). https://www.researchgate.net/publication/308457837, DOI: 10.1109/MLHPC.2016.006 26 10.1109/MLHPC.2016.006 false true Reference 35175895 /api/reference/35175895 27. Khorsand P, Chance F (2008) Transient responses to rapid changes in mean and variance in spiking models. PLoS ONE 3(11):3786. https://doi.org/10.1371/journal.pone.0003786, DOI: 10.1371/journal.pone.0003786 27 10.1371/journal.pone.0003786 false true Reference 35175896 /api/reference/35175896 28. Koch C (1999) Biophysics of computation. Oxford University Press, Oxford 28 false true Reference 35175897 /api/reference/35175897 29. Koch C, Poggio TA (1983) A theoretical analysis of electrical properties of spines. Proc R Soc Lond B 218:455–477, DOI: 10.1098/rspb.1983.0051 29 10.1098/rspb.1983.0051 false true Reference 35175898 /api/reference/35175898 30. Kumar S et al (2013) Acceleration of an asynchronous message driven programming paradigm on IBM blue gene/Q (IEEE, Boston). https://www.ieeexplore.ieee.org/abstract/document/6569854, DOI: 10.1109/IPDPS.2013.83 30 10.1109/IPDPS.2013.83 false true Reference 35175899 /api/reference/35175899 31. Kunkel S et al (2014) Spiking network simulation code for petascale computers. Front Neuroinform 8:78. https://doi.org/10.3389/fninf.2014.00078, DOI: 10.3389/fninf.2014.00078 31 10.3389/fninf.2014.00078 false true Reference 35175900 /api/reference/35175900 32. Li M, Tsien JZ (2017) Neural code-neural self-information theory on how cell-assembly code rises from spike time and neuronal variability. Front Cell Neurosci. https://doi.org/10.3389/fncel.2017.00236, DOI: 10.3389/fncel.2017.00236 32 10.3389/fncel.2017.00236 false true Reference 35175901 /api/reference/35175901 33. Losonczy A, Magee J (2006) Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50:291–307, DOI: 10.1016/j.neuron.2006.03.016 33 10.1016/j.neuron.2006.03.016 false true Reference 35175902 /api/reference/35175902 34. Luk W (2019) Imperial College London, textbook. http://www.imperial.ac.uk/~wl/teachlocal/cuscomp/notes/chapter2.pdf. Accessed 14 Dec 2020 34 false true Reference 35175903 /api/reference/35175903 35. Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14(11):2531–2560. https://doi.org/10.1162/089976602760407955, DOI: 10.1162/089976602760407955 35 10.1162/089976602760407955 false true Reference 35175904 /api/reference/35175904 36. Markovic D, Mizrahi A, Querlioz D, Grollier J (2020) Physics for neuromorphic computing. Nat Rev Phys 2:499–510, DOI: 10.1038/s42254-020-0208-2 36 10.1038/s42254-020-0208-2 false true Reference 35175905 /api/reference/35175905 37. McKenzie S et al (2021) Preexisting hippocampal network dynamics constrain optogenetically induced place fields. Neuron. https://doi.org/10.1101/803577, DOI: 10.1101/803577 37 10.1101/803577 false true Reference 35175906 /api/reference/35175906 38. Minkowski H (1908) Die Grundgleichungen für die electromagnetischen Vorgänge in bewegten Körpern. Nachrichten von der Königlichen Gesellschaft der Wissenschaften zu Göttingen (in German), pp 53–111 (1908) 38 false true Reference 35175907 /api/reference/35175907 39. Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern. https://doi.org/10.1007/s00422-008-0233-1, DOI: 10.1007/s00422-008-0233-1 39 10.1007/s00422-008-0233-1 false true Reference 35175908 /api/reference/35175908 40. Pajevic S, Basser P, Fields R (2014) Role of myelin plasticity in oscillations and synchrony of neuronal activity. Neuroscience 276:135–147. https://doi.org/10.1016/j.neuroscience.2013.11.007, DOI: 10.1016/j.neuroscience.2013.11.007 40 10.1016/j.neuroscience.2013.11.007 false true Reference 35175909 /api/reference/35175909 41. Patterson D, Hennessy J (2017) (eds) Computer Organization and design. RISC-V Edition (Morgan Kaufmann) 41 false true Reference 35175910 /api/reference/35175910 42. Pereda AE (2014) Electrical synapses and their functional interactions with chemical synapses. Nat Rev Neurosci 15:250–263, DOI: 10.1038/nrn3708 42 10.1038/nrn3708 false true Reference 35175911 /api/reference/35175911 43. Plenz D, Aertsen A (1996) Neural dynamics in cortex-striatum ci-cultures - II. Spatiotemporal characteristics of neural activity. Neuroscience 70(4):893–924, DOI: 10.1016/0306-4522(95)00405-X 43 10.1016/0306-4522(95)00405-X false true Reference 35175912 /api/reference/35175912 44. Prut Y et al (1998) Spatiotemporal structure of cortical activity: properties and behavioral relevance. J Neurophysiol 79:2857–2874, DOI: 10.1152/jn.1998.79.6.2857 44 10.1152/jn.1998.79.6.2857 false true Reference 35175913 /api/reference/35175913 45. Schliebs S, Kasabov NK (2013) Evolving spiking neural networks: a survey. Evol Syst 4(2):2. https://doi.org/10.1007/s12530-013-9074-9, DOI: 10.1007/s12530-013-9074-9 45 10.1007/s12530-013-9074-9 false true Reference 35175914 /api/reference/35175914 46. Schuman CD et al (2017) A survey of neuromorphic computing and neural networks in hardware. arXiv:org/abs/1705.06963. Accessed 10 Sept 2022 46 false true Reference 35175915 /api/reference/35175915 47. Simon H (2014) Why we need Exascale and why we won’t get there by 2020. https://www.researchgate.net/publication/261879110 _Why_we_need_Exascale_and_why_we_won’t_get _there_by_2020. Accessed 23 Oct 2021 47 false true Reference 35175916 /api/reference/35175916 48. Singh JP, Hennessy JL, Gupta A (1993) Scaling parallel programs for multiprocessors: methodology and examples. Computer 26(7):42–50. https://doi.org/10.1109/MC.1993.274941, DOI: 10.1109/MC.1993.274941 48 10.1109/MC.1993.274941 false true Reference 35175917 /api/reference/35175917 49. Singh P et al (2021) Cytoskeletal filaments deep inside a neuron are not silent: they regulate the precise timing of nerve spikes using a pair of vortices. Symmetry 13(5):821. https://doi.org/10.3390/sym13050821, DOI: 10.3390/sym13050821 49 10.3390/sym13050821 false true Reference 35175918 /api/reference/35175918 50. Stemmler M, Koch C (1999) How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nat Neurosci 2:521–527, DOI: 10.1038/9173 50 10.1038/9173 false true Reference 35175919 /api/reference/35175919 51. Sterling P, Laughlin S (2017) Principles of neural design, 1st edn. Boca Raton, MIT Press 51 false true Reference 35175920 /api/reference/35175920 52. Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80–83, DOI: 10.1038/nature06932 52 10.1038/nature06932 false true Reference 35175921 /api/reference/35175921 53. Strukov D, Indiveri G, Grollier J, Fusi S (2019) Building brain-inspired computing. Nat Commun 10(12):4838. https://doi.org/10.1038/s41467-019-12521-x, DOI: 10.1038/s41467-019-12521-x 53 10.1038/s41467-019-12521-x false true Reference 35175922 /api/reference/35175922 54. TOP500. Top500 list of supercomputers (2021). https://www.top500.org/lists/top500/. Accessed 24 Oct 2021 54 false true Reference 35175923 /api/reference/35175923 55. van Albada SJ et al (2018) Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model. Front Neurosci 12:291, DOI: 10.3389/fnins.2018.00291 55 10.3389/fnins.2018.00291 false true Reference 35175924 /api/reference/35175924 56. Végh J (2019) How Amdahl’s Law limits performance of large artificial neural networks. Brain Inform 6:1–11, DOI: 10.1186/s40708-019-0097-2 56 10.1186/s40708-019-0097-2 false true Reference 35175925 /api/reference/35175925 57. Végh J (2020a) Finally, how many efficiencies the supercomputers have? J Supercomput 76(12):9430–9455, DOI: 10.1007/s11227-020-03210-4 57 10.1007/s11227-020-03210-4 false true Reference 35175926 /api/reference/35175926 58. Végh J (2020b) Why do we need to introduce temporal behavior in both modern science and modern computing. Global J Comput Sci Technol 20(1):13–29 58 false true Reference 35175927 /api/reference/35175927 59. Végh J (2021a) A model for storing and processing information in technological and biological computing systems, vol 21, FCS4404, IEEE Las Vegas 59 false true Reference 35175928 /api/reference/35175928 60. Végh J (2021b) Revising the classic computing paradigm and its technological implementations. Informatics 8(4):71. https://doi.org/10.3390/informatics8040071, DOI: 10.3390/informatics8040071 60 10.3390/informatics8040071 false true Reference 35175929 /api/reference/35175929 61. Végh J (2021c) Which scaling rule applies to artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06456-y, DOI: 10.1007/s00521-021-06456-y 61 10.1007/s00521-021-06456-y false true Reference 35175930 /api/reference/35175930 62. Végh J, Berki AJ (2020) On the spatiotemporal behavior in biology-mimicking computing systems. https://www.researchgate.net/publication/344325571_On_the_Spatiotemporal_Behavior_in_Biology-Mimicking_Computing_Systems. Accessed 24 Oct 2021, DOI: 10.21203/rs.3.rs-88297/v2 62 10.21203/rs.3.rs-88297/v2 false true Reference 35175931 /api/reference/35175931 63. Végh J, Berki AJ (2022) Towards generalizing the information theory for neural communication. Entropy 24(8):1086. https://doi.org/10.3390/e24081086, DOI: 10.3390/e24081086 63 10.3390/e24081086 false true Reference 35175932 /api/reference/35175932 64. Végh J, Arabnia HR, Deligiannidis L, Tinetti FG, Tran Q-N (eds) (2021) Introducing temporal behavior to computing science. Advances in Software Engineering, Education, and e-Learning, pp 471–491 (Springer International Publishing), DOI: 10.1007/978-3-030-70873-3_33 64 10.1007/978-3-030-70873-3_33 false true Reference 35175933 /api/reference/35175933 65. von Neumann J (1993) First draft of a report on the EDVAC. IEEE Ann Hist Comput 15(4):27–75. https://doi.org/10.1109/85.238389, DOI: 10.1109/85.238389 65 10.1109/85.238389 false true Reference 35175934 /api/reference/35175934 66. Walter S (2008) Hermann Minkowski and the scandal of spacetime. ESI News 1(3):6–8 66 false true Reference 35175935 /api/reference/35175935 67. Waser R (2012) (ed.) Advanced electronics materials and novel devices nanoelectronics and information technology (Wiley-VCH) 67 false true Reference 35175936 /api/reference/35175936 68. Williams MR (1993) The origins, uses, and fate of the EDVAC. IEEE Ann Hist Comput 15(1):22–38. https://doi.org/10.1109/85.194089, DOI: 10.1109/85.194089 68 10.1109/85.194089 false true Reference 35175937 /api/reference/35175937 69. Xu K et al (2020) Learning in the frequency domain. arXiv.org/abs/2002.12416 69 false true /api/publication/33199928 Végh János et al. On the Role of Speed in Technological and Biological Information Transfer for Computations. (2022) ACTA BIOTHEORETICA 0001-5342 1572-8358 70 4<div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" mtid="10007792"><a href="/gui2/?type=authors&mode=browse&sel=10007792" target="_blank">Végh János ✉ (<span class="authorship-author-name">Végh János</span> <span class="authorAux-mtmt"> Atomfizika</span>) </a> </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10084426"><a href="/gui2/?type=authors&mode=browse&sel=10084426" target="_blank">Berki Ádám József^* ✉ (<span class="authorship-author-name">Berki Ádám József</span> <span class="authorAux-mtmt"> Neurológia, idegtudományok</span>) </a> </span> <span class="author-affil"><span title="Semmelweis Egyetem">SE</span>/<span title="Általános Orvostudományi Kar">AOK</span>/<span title="Klinikum">K</span>/Neurológiai Klinika; <span title="Semmelweis Egyetem">SE</span>/Doktori Iskola</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33199928" target="_blank">On the Role of Speed in Technological and Biological Information Transfer for Computations</a></div> <div> <span class="journal-title">ACTA BIOTHEORETICA</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/0001-5342">0001-5342</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1572-8358">1572-8358</a>)</span>: <span class="journal-volume">70</span> <span class="journal-issue">4</span> <span class="page"> Paper 26. 25 p. </span> <span class="year">(2022)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1007/s10441-022-09450-6" target="_blank" href="https://doi.org/10.1007/s10441-022-09450-6"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000873837400001" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000873837400001"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85140580130" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85140580130"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="36287247" target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=36287247&dopt=Abstract"> PubMed </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="https://www.preprints.org/manuscript/202103.0414/v1" target="_blank" href="https://www.preprints.org/manuscript/202103.0414/v1"> Egyéb URL </a> </span> </span> <OnlyViewableByAuthor><div class="ratings"> <div class="journal-subject">Folyóirat szakterülete: Scopus - Agricultural and Biological Sciences (miscellaneous)&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q2</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Philosophy&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q2</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Applied Mathematics&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q3</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Biochemistry, Genetics and Molecular Biology (miscellaneous)&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q3</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Environmental Science (miscellaneous)&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q3</div> <div class="journal-subject">Folyóirat szakterülete: Scopus - Medicine (miscellaneous)&nbsp;&nbsp;&nbsp;SJR indikátor:&nbsp;Q3</div> </div></OnlyViewableByAuthor> <div class="publication-citation" style="margin-left: 0.5cm;"> <span title="Nyilvános idézőközlemények összesen, említések nélkül" class="citingPub-count">Nyilvános idéző összesen: 4</span> | Független: 1 | Függő: 3 | Nem jelölt: 0 | WoS jelölt: 2 | Scopus jelölt:&nbsp;4 | WoS/Scopus jelölt:&nbsp;4 | DOI jelölt:&nbsp;4 </div> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;33199928&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 4 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33199928</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Forrás Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="funder"> (136496) </div> <div class="lastModified">Utolsó módosítás: 2026.05.14. 11:05 Gere Tamásné (SE_KK_Admin5_GT, admin) </div> </div></div>