Name and surname:
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prof. RNDr. Ľubica Beňušková, PhD.
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Document type:
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Research/art/teacher profile of a person
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The name of the university:
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Comenius University Bratislava
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The seat of the university:
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Šafárikovo námestie 6, 818 06 Bratislava
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III.a - Occupation-position | III.b - Institution | III.c - Duration |
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Professor | Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia | 2016 - onwards |
Lecturer / Associate Professor | University of Otago, Dunedin, New Zealand | 2008 - 2015 |
Research Fellow | Auckland University of Technology, Auckland, New Zealand | 2003 - 2007 |
Associate Professor | Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia | 2001 - 2003 |
Senior Research Fellow | Faculty of Electrotechnics and Informatics, Slovak University of Technology, Bratislava, Slovakia | 1994 - 2001 |
Research Fellow | Institute of Animal Biochemistry and Genetics, Slovak Academy of Sciences | 1988 - 1994 |
Research Assistant | University Hospital, Bratislava, Old City | 1982 - 1988 |
V.1.a - Name of the profile course | V.1.b - Study programme | V.1.c - Degree | V.1.d - Field of study |
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Computational Cognitive Neuroscience | Cognitive Science | MSc | Informatics |
V.5.a - Name of the course | V.5.b - Study programme | V.5.c - Degree | V.5.d - Field of study |
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Introduction to Artificial Intelligence | Applied Informatics | BSc | Informatics |
Computational Neuroscience | Cognitive Science | MSc | Informatics |
Tino P, Cernansky M, Benuskova L (2004) Markovian architectural bias of recurrent neural networks. IEEE Transactions on Neural Networks 15(1): 6-15. (Q1, IF=10.45) Number of citations: 212 (Google Scholar)
Wysoski SG, Benuskova L, Kasabov N (2010) Evolving spiking neural networks for audiovisual information processing. Neural Networks, 23(7): 819-835. (Q1, IF=8.05) Number of citations: 193 (Google Scholar)
Wysoski SG, Benuskova L, Kasabov N (2008) Fast and adaptive networks of spiking neurons for multi-view visual pattern recognition. Neurocomputing 71(13-15): 2563-2575. (Q1, IF=5.7) Number of citations: 137 (Google Scholar)
Benuskova L, Diamond ME, Ebner FF (1994) Dynamic synaptic modification threshold: Computational model of experience-dependent plasticity in adult rat barrel cortex. Proceedings of the National Academy of Sciences of the USA (PNAS), 91(11): 4791-4795. (Q1, IF=11.2) Number of citations: 63 (Google Scholar)
Benuskova L, Rema V, Armstrong-James M and Ebner FF (2001) Theory for normal and impaired experience-dependent plasticity in neocortex of adult rats. Proceedings of the National Academy of Sciences of the USA (PNAS), 98(5): 2797-2802. (Q1, IF=11.2) Number of citations: 35 (Google Scholar)
Tomko M, Benuskova L, Jedlicka P (2024) A voltage-based event-timing-dependent plasticity rule accounts for LTP subthreshold and suprathreshold for dendritic spikes in CA1 pyramidal neurons. Journal of Computational Neuroscience, 52, pp. 125–131. doi: 10.1007/s10827-024-00868-0. (Q3, IF=1.2)
Tomko M, Benuskova L, Jedlicka P (2021) A new reduced-morphology model for CA1 pyramidal cells and its validation and comparison with other models using HippoUnit. Scientific Reports | Nature 11, 7615. doi: 10.1038/s41598-021-87002-7. (Q1, IF=4.38) Number of citations: 8 (Google Scholar)
Tomko M, Jedlicka P, Benuskova L (2020) Meta-STDP rule stabilizes synaptic weights under in vivo-like ongoing spontaneous activity in a computational model of CA1 pyramidal cell. In: Farkas I, Masulli P, Wermter S (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. Springer, Lecture Notes in Computer Science, vol 12397, pp 670-680. (Q3, IF=1.27)
Markosova M, Rudolf B, Nather P, Benuskova L (2020) Network models for changing degree distributions of functional brain networks. Neural Networks World, 30(5), pp. 309-332. (Q4, IF=1.02)
Shirrafiardekani A, Frauendiener J, Moustafa AA, Benuskova L (2019) Computational examination of synaptic plasticity and metaplasticity in hippocampal dentate granule neurons. In: V. Cutsuridis, B.P. Graham, S. Cobb, I. Vida (Eds), Hippocampal Microcircuits: A Computational Modeler's Resource Book, 2nd Ed, Springer Nature Switzerland AG, pp. 701-737.
Tino P , Cernansky M, Benuskova L (2004) : [o1] 2020 - Zhao, A. - Zhang, N. - Xi, M. - Sun, J. - Dong, M. - Robust H-infinity filtering for Markovian jumping static neural networks with time varying delays. - In: Journal of Algorithms and Computational Technology, Vol. 14, 2020 ; Art. No. 1748302620931340 ; SCI ; SCOPUS [o1] 2021 - Carta, A. - Sperduti, A. - Bacciu, D. - Encoding-based memory for recurrent neural networks. - In: Neurocomputing, Vol. 456, 2021 ; 407-420 ; SCI ; SCOPUS [o1] 2021 - Chen, Y. - Ren, J. - Zhao, X. - Xue, A. - State estimation of Markov jump neural networks with random delays by redundant channels. - In: Neurocomputing, Vol. 453, 2021 ; 493-501 ; SCOPUS [o1] 2021 - Shen, H. - Xing, M. - Wu, Z.- Cao, J. - Huang, T. - L-l State Estimation for Persistent Dwell-Time Switched Coupled Networks Subject to Round-Robin Protocol. - In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 5, 2021 ; art. no. 9108620, 2002-2014 ; SCI ; SCOPUS [o1] 2021 - Smith, Q. J. - Valverde, R. - A perceptron based neural network data analytics architecture for the detection of fraud in credit card transactions in financial legacy systems. - In: WSEAS Transactions on Systems and Control, Vol. 16, 2021 ; 358-374 ; SCOPUS
Wysoski SG, Benuskova L, Kasabov N (2010) : [o1] 2020 - Gupta, J. - Koppad, D. - A survey on Memristor and CMOS based Spiking Neural Networks. - In: Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020 [rNew York : IEEE, 2020 ; 1052-1058 ; SCOPUS [o1] 2020 - Lobo, J. L. - Oregi, I. - Bifet, A. - Del Ser, J. - Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning. - In: Neural Networks, Vol. 123, 2020 ; 118-133 ; SCI ; SCOPUS [o1] 2020 - Rostro-Gonzalez, H. - Lauterio-Cruz, J. P. - Pottiez, O. - Modelling Neural Dynamics with Optics: A New Approach to Simulate Spiking Neurons through an Asynchronous Laser. - In: Electronics, Vol. 9, No. 11, 2020 ; Art. No. 1853 ; SCI ; SCOPUS [o1] 2020 - Xing, Y. - Di Caterina, G. - Soraghan, J. - A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition. - In: Frontiers in Neuroscience, Vol. 14, 2020 ; Art. No. 590164 ; SCI ; SCOPUS [o1] 2020 - Zhang, M. - Luo, X. - Chen, Y. - Wu, J. - Belatreche, A. - Pan, Z. - Qu, H. - Li, H. - An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing. - In: IEEE Journal of Selected Topics in Signal Processing, Vol. 14, No. 3, 2020 ; 592-602 ; SCI ; SCOPUS
Wysoski SG, Benuskova L, Kasabov N (2008): [o1] 2020 - Salleh, A. S. M. - Hamed, H. N. A. - Isa, M. A. - Haron, H. - Hydrofluoroether Impurities-Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network. - In: IEEE Access, Vol. 8, 2020 ; 216419-216436 ; SCI ; SCOPUS [o1] 2020 - Taherkhani, A. - Belatreche, A. - Li, Y. - Cosma, G. - Maguire, L. P. - McGinnity, T. M. - A review of learning in biologically plausible spiking neural networks. - In: Neural Networks, Vol. 122, 2020 ; 253-272 ; SCI ; SCOPUS [o1] 2020 - Wang, S. - Wang, L. - Kang, Z. – Qu, L. - Li, S. - Su, J. - A software-hardware coexploration framework for optimizing communication in neuromorphic processor. - In: Advanced Computer
Architecture, ACA 2020; Communications in Computer and Information Science, Vol. 1256 . - Singapore : Springer Nature, 2020 ; 87-100 ; SCOPUS [o1] 2020 - Yang, D. - Zhong, X. - Gu, D. - Peng, X. - Hu, H. - Unsupervised framework for depth estimation and camera motion prediction from video. - In: Neurocomputing, Vol. 385, 2020 ; 169-185 ; SCI ; SCOPUS [o1] 2020 - Zhou, Q. - Ren, C. - Qi, S. - An Imbalanced R-STDP Learning Rule in Spiking Neural Networks for Medical Image Classification. - In: IEEE Access, Vol. 8, 2020 ; s224162-224177 ; SCI ; SCOPUS
Benuskova L, Diamond ME and Ebner FF (1994): [o1] 2012 - Hulme, S. - Jones, O. - Ireland, D. - Abraham, W. - Calcium-dependent but action potential-independent BCM-like metaplasticity in the hippocampus. - In: Journal of Neuroscience, Vol. 32, No. 20, 2012 ; 6785-6794 ; SCI ; SCOPUS [o1] 2013 - Wen, J. - DeBlois, M. - Barth, A. - Initiation, labile, and stabilization phases of experience-dependent plasticity at neocortical synapses. - In: Journal of Neuroscience, Vol. 33, No. 19, 2013 ; 8483-8493 ; SCI ; SCOPUS [o1] 2017 - Yee, A. - Hsu, Y. - Chen, L. - A metaplasticity view of the interaction between homeostatic and hebbian plasticity. - In: Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 372, No. 1715, 2017 ; Art. No. 20160155 ; SCI ; SCOPUS [o1] 2018 - Chaudhary, R. - Rema, V. - Deficits in Behavioral Functions of Intact Barrel Cortex Following Lesions of Homotopic Contralateral Cortex. - In: Frontiers in Systems Neuroscience, Vol. 12, 2018 ; Art. No. 57 ; SCI ; SCOPUS [o1] 2019 - Yang, R. - Huang, H. M. - Guo, X. - Memristive Synapses and Neurons for Bioinspired Computing. - In: Advanced Electronic Materials, Vol. 5, No. 9, 2019 ; Art. No. 1900287 ; SCI ;
SCOPUS
Benuskova L et al (2001): [o1] 2004 - Catania, K. C. - Remple, F. E. - Tactile foveation in the star-nosed mole. - In: Brain Behavior and Evolution, Vol. 63, No. 1, 2004 ; 1-12 ; SCI ; SCOPUS [o1] 2004 - Mazza, M. - De Pinho, M. - Piqueira, J. R. C. - Roque, A. C. - A dynamical model of fast cortical reorganization. - In: Journal of Computational Neuroscience, Vol. 16, No. 2, 2004 ; 177-201 ; SCI ; SCOPUS [o1] 2006 - Miller, M. W. - Effect of prenatal exposure to ethanol on glutamate and GABA immunoreactivity in macaque somatosensory and motor cortices: Critical timing of exposure. - In: Neuroscience, Vol. 138, No. 1, 2006 ; 97-107 ; SCI ; SCOPUS [o1] 2009 - Hood, D. - Ramesh, A. - Aschner, M. - Polycyclic aromatic hydrocarbons: Exposure from emission products and from terrorist attacks on US targets -implications for developmental central nervous system toxicity. - In: Handbook of Toxicology of Chemical Warfare Agents . - San Diego : Elsevier, 2009 ; S. 229-243 ; BKCI-S ; SCOPUS [o1] 2017 - Gavin, D. P. - Grayson, D. R. - Varghese, S. P. - Guizzetti, M. - Chromatin Switches during Neural Cell Differentiation and Their Dysregulation by Prenatal Alcohol Exposure. - In: Genes, Vol. 8, No. 5, 2017 ; Art. No. 137 ; SCI ; SCOPUS
2017-2019, VEGA 1/0039/17: Complex networks and their application to real-world problems (Principal Investigator)
VII.a - Activity, position | VII.b - Name of the institution, board | VII.c - Duration |
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Member | Board of Master program in Cognitive Science | 2018 - onwards |
Member of the Editorial Board of the international scientific journal (http://www.nnw.cz/board.html) | Neural Network World | 2003 - onwards |
Chair of the board of the Peter Fedor Fund (http://www.naturaoz.org/FondPetraFedora.html) | Natura, o.z. | 2001 - onwards |
VIII.a - Name of the institution | VIII.b - Address of the institution | VIII.c - Duration (indicate the duration of stay) | VIII.d - Mobility scheme, employment contract, other (describe) |
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CEEPUS | Eotvos Lorand University, Budapest, Hungary | 19.06.2017-24.06.2017 | CEEPUS mobility |
Vanderbilt University | Nashville, TN, USA | 07/1999 - 09/1999, 07/1998 - 09/1998, 07/1997 - 09/1997, 07/1996 - 09/1996 | U.S.-Slovak Science and Technology Program |
Brown University | Providence, RI, USA | 09/1996 | U.S.-Slovak Science and Technology Program |
SISSA | Trieste, Italy | 11/1995 -12/1995 | SISSA travel grant |
Vanderbilt University | Nashville, TN, USA | 09/1991 - 12/1993 | Vanderbilt University postgraduate scholarship |