Publications of Ľubica Beňušková


2016
  • Hananeia N, Benuskova L (2016) Computational simulation of dentate gyrus granule cell - the role of metaplasticity. Neurocomputing 175: 300-309. link

2015
  • Jedlicka P, Benuskova L, Abraham WC (2015) A voltage-based STDP rule combined with fast BCM-like metaplasticity accounts for LTP and concurrent "heterosynaptic" LTD in the dentate gyrus in vivo. PLoS Computational Biology 11(11): e1004588. doi:10.1371/journal.pcbi.1004588. link
  • Nido GS, Ryan MM, Benuskova L, Williams JM (2015) Dynamical properties of gene regulatory networks involved in long-term potentiation. Frontiers in Molecular Neuroscience 8:42. doi: 10.3389/fnmol.2015.00042 link
  • Guise M, Knott A, Benuskova L (2015) Enhanced polychronization in a spiking network with metaplasticity. Frontiers in Computational Neuroscience. 9:9. doi: 10.3389/fnagi.2015.00009 link
  • Tino P, Benuskova L, Sperduti A (2015) Artificial neural network models. In: J. Kacprzyk, W. Pedrycz (Eds), Springer Handbook of Computational Intelligence, Springer, Dordrecht / Heidelberg, pp. 455-472. ISBN 978-3-662-43504-5. link
  • Navrat P, Benuskova L, Bielikova M, Kapustik I, Koskova G, Pospichal J (2015) Umela inteligencia , Vydavatelstvo STU, Bratislava. ISBN 978-80-227-4344-0. link

2014
  • McCarthy P, Benuskova L, Franz EA (2014) The age-related posterior-anterior shift as revealed by voxelwise analysis of functional brain networks. Frontiers in Aging Neuroscience. 6:301. doi: 10.3389/fnagi.2014.00301 Link
  • Guise M, Knott A, Benuskova, L (2014) A Bayesian model of polychronicity. Neural Computation 26(9): 2052-2073 link
  • Benuskova L, Kasabov N (2014) Computational neurogenetic modeling: gene-dependent dynamics of cortex and idiopathic epilepsy. In: N. Kasabov (Ed), Springer Handbook of Bio-/Neuroinformatics, Springer-Verlag, Berlin / Heidelberg, pp. 969-991. ISBN 978-3-642-30573-3. link

2013
  • Guise M, Knott A, Benuskova, L (2013) Evidence for response consistency supports polychronous neural groups as an underlying mechanism for representation and memory. In S. Cranefield, A. Nayak (Eds.), AI 2013, LNAI 8272, Springer, Heidelberg, pp. 86-97. link

2012
2011
  • Takac M, Benuskova L, Knott A (2011) A Sentence generation network that learns surface and abstract syntactic structures. In: T. Honkela, W. Duch, M. Girolami, S. Kaski (Eds) ICANN 2011, Part II, LNCS 6792, Springer-Verlag, Berlin / Heidelberg, pp. 341-348. ISBN 978-3-642-21737-1. link
  • Wysoski SG, Benuskova L, Kasabov N (2011) Brain-Like system for audiovisual person authentication based on time-to-first spike coding. In: B. Igelnik (ed) Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, IGI Global, pp. 384-412. ISBN13 9781609605513. link

2010
  • Wysoski SG, Benuskova L, Kasabov N (2010) Evolving spiking neural networks for audiovisual information processing. Neural Networks, 23(7): 819-835. link
  • Wysoski SG, Benuskova L and Kasabov N (2010) Brain-like evolving spiking neural networks for multimodal information processing. In: A. Hanazawa et al. (eds) Brain-Inspired Information Technology, Studies in Computational Intelligence, vol. 266, Springer-Verlag, Berlin / Heidelberg, pp. 15-27. ISBN 978-3-642-04024-5. link

2009
  • Makula M, Benuskova L (2009) Interactive visualisation of oligomer frequency in DNA. Computing and Informatics, vol. 28, pp. 695-710. link
  • Markosova M, Franz L, Benuskova (2009) Topology of brain functional networks: towards the role of genes. In: M. Koeppen, N. Kasabov, G. Coghill (Eds), Advances in Neuro-Information Processing, ICONIP 2008, LNCS 5506, Springer, Berlin/Heidelberg, pp. 111-118. ISBN 978-3-642-02489-4. link
  • Cernansky M, Benuskova L (2009) Training recurrent connectionist models on symbolic time series In: M. Koeppen, N. Kasabov, G. Coghill (Eds) Advances in Neuro-Information Processing, ICONIP'2008, Lecture Notes in Computer Science, vol. 5506, Springer-Verlag, Berlin/Heidelberg, pp. 285-292. ISBN 978-3-642-02489-4. link
  • Cernansky M, Makula M, Benuskova L (2009) Improving the state space organization of untrained recurrent neural networks. In: M. Koeppen, N. Kasabov, G. Coghill (Eds) Advances in Neuro-Information Processing, ICONIP'2008, Lecture Notes in Computer Science, vol. 5506, Springer-Verlag, Berlin/Heidelberg, pp. 671-678. ISBN 978-3-642-02489-4. link

2008
  • Benuskova L, Kasabov N (2008) Modeling brain dynamics using computational neurogenetic approach. Cognitive Neurodynamics 2(4): 319-334. link
  • 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. link
  • Kasabov N, Jain V, Benuskova L (2008) Integrating evolving brain-gene ontology and connectionist-based system for modeling and knowledge discovery. Neural Networks 21(2-3): 266-275. ISSN 0893-6080. link
  • Wysoski SG, Benuskova L, Kasabov N (2008) Adaptive spiking neural networks for audiovisual pattern recognition. In: M. Ishikawa et al (Eds) ICONIP'2007, Part II, Lecture Notes in Computer Science, vol. 4985, Springer-Verlag, Berlin/Heidelberg, pp. 406-415. ISBN 978-3-540-69159-4. link
  • Makula M, Benuskova L (2008) Analysis and visualization of the dynamics of recurrent neural networks for symbolic sequences processing. Artificial Neural Networks - ICANN'08, Lecture Notes in Computer Science, vol. 5164, pp. 577-586, Springer Berlin / Heidelberg. ISBN 978-3-540-87558-1. link

2007
  • Benuskova L and Kasabov N (2007) Computational Neurogenetic Modeling. Springer, New York. ISBN 978-0-387-48353-5. book review in BioEssays
  • Abraham WC, Logan B, Wolff A, Benuskova L (2007) "Heterosynaptic" LTD in the dentate gyrus of anesthetized rat requires homosynaptic activity. Journal of Neurophysiology 98: 1048-1051. ISSN 0022-3077. link
  • Benuskova L and Abraham WC (2007) STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity. Journal of Computational Neuroscience 22(2): 129-133. ISSN 0929-5313. Online from 12 Oct 2006
  • Benuskova L and Kasabov N (2007) Modeling L-LTP based on changes in concentration of pCREB transcription factor. Neurocomputing 70(10-12) 2035-2040. ISSN 0925-2312. Online from 15 Nov 2006
  • Cernansky M, Makula M, and Benuskova L (2007) Organization of the state space of a simple recurrent neural network before and after training on recursive linguistic structures. Neural Networks 20(2):236-244. ISSN 0893-6080. link
  • Wysoski SG, Benuskova L and Kasabov N (2007) Text-independent speaker authentication with spiking neural networks. In: J. Marques de Sa et al (Eds) ICANN'2007, Part II, Lecture Notes in Computer Science, vol. 4669, Springer-Verlag, Berlin/Heidelberg, pp. 758-767. link
  • Benuskova L (2007) Neurovedne okno do vedomia. In: Mysel, inteligencia a zivot, V. Kvasnicka, P. Trebaticky, J. Pospichal, J. Kelemen (eds), Vydavatelstvo STU, Bratislava, pp. 145-156. ISBN 978-80-227-2643-6. (about this book)

2006
  • Benuskova L, Jain V, Wysoski SG and Kasabov N (2006) Computational neurogenetic modeling: a pathway to new discoveries in genetic neuroscience. Intl. Journal of Neural Systems, 16(3): 215-227. ISSN 0129-0657. link
  • Benuskova L, Wysoski SG and Kasabov N (2006) Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations. Proc. Intl. Joint Conf. Neural Net. (IJCNN 2006), pp. 9388-9394. ISBN 0-7803-9490-9.
  • Wysoski SG and Benuskova L (2006) Biologically Realistic Neural Networks and Adaptive Visual Information Processing. Bulletin of Applied Computing and Information Technology 4(2): A3. ISSN 1176-4120. (html)
  • Wysoski SG, Benuskova L and Kasabov N (2006) On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition. In: S. Kollias et al (Eds) Intl. Conf. Art. Neural Net. - ICANN 2006. Lecture Notes in Computer Science, vol. 4131, Springer-Verlag, Berlin/Heidelberg, pp. 61-70. ISBN 978-3-540-38625-4. link
  • Wysoski SG, Benuskova L, Kasabov N (2006) Adaptive learning procedure for a network of spiking neurons and visual pattern recognition. In: Advanced Concepts for Intelligent Vision Systems (ACIVS), Lecture Notes in Computer Science, vol. 4179, pp. 1133-1142, Springer, Berlin/Heidelberg. ISBN 978-3-540-44630-9. link
  • Kasabov N and Benuskova L (2006) Theoretical and Computational Models for Neuro, Genetic, and Neuro-Genetic Information Processing. In: Handbook of Computational and Theoretical Nanotechnology , M. Rieth and W. Schommers (eds), vol 6, chap 17, pp. 779-816, American Scientific Publishers, Los Angeles. ISBN 1-58883-048-9.
  • Navrat P, Bielikova M, Benuskova L, Kapustik I, Unger M (2006) Umela inteligencia, 2. vydanie Vydavatelstvo STU, Bratislava. ISBN 80-227-2354-1 (1. vydanie, 2002, ISBN 80-227-1645-6) summary

2005
  • Kasabov N, Benuskova L and Wysoski SG (2005) Biologically plausible computational neurogenetic models: modelling interaction between genes, neurons and neural networks. Journal of Computational and Theoretical Nanoscience , 2(4): 569-573. ISSN 1546-198X. link
  • Kasabov N, Benuskova L and Wysoski SG (2005) Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques. In: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, LNCS 3697, W. Duch, J. Kacprzyk, E. Oja, S. Zadrozny (eds), Springer-Verlag, Berlin Heidelberg, pp. 509-514. ISBN 3-540-28755-8. link
  • Kasabov N, Benuskova L and Wysoski SG (2005) A computational neurogenetic model of a spiking neuron. Proc. IEEE Intl. Joint Conference on Neural Networks (IJCNN 2005) , vol. 1, pp. 446-451. ISBN 0-7803-9049-0. link
  • Benuskova L (2005) Kde sa jazyk stretava s vedomim. In: Rybar J, Kvasnicka V, Farkas I (eds) Jazyk a kognicia. Kalligram, Bratislava, pp. 235-261. ISBN 80-7149-716-9.

2004
  • Tino P , Cernansky M and Benuskova L (2004) Markovian architectural bias of recurrent neural networks. IEEE Transactions on Neural Networks 15(1): 6-15. ISSN 1045-9227. link
  • Makula M, Cernansky M and Benuskova L (2004) Approaches based on Markovian architectural bias in recurrent neural networks. In: SOFSEM'2004 - Theory and Practise of Computer Science. Lecture Notes in Computer Science, vol. 2932, P. Van Emde Boas, J. Pokorny, M. Bielikova, J. Stuller (eds), Springer-Verlag, Berlin Heidelberg, pp. 257-264. ISBN 3-540-20779-1. link
  • Cernansky M, Makula M and Benuskova L (2004) Processing symbolic sequences by recurrent neural networks trained by Kalman filter based algorithms. In: SOFSEM 2004: Theory and Practice of Computer Science. Vol. II. , P. Van Emde Boas, J. Pokorny, M. Bielikova, J. Stuller (eds), Matfyzpress. Praha, 2004, pp. 58-65. ISBN 80-86732-19-3.

2003
  • Cernansky M and Benuskova L (2003) Simple recurrent network trained by RTRL and extended Kalman filter algorithms. Neural Network World 13(3): 223-234. ISSN 1210-0552. pdf
  • Makula M and Benuskova L (2003) Analysis of state space of RNNs trained on a chaotic symbolic sequence. Neural Network World 13(3): 267-276. ISSN 1210-0552. pdf
  • Benuskova L, Lacko D. (2003) Word segmentation: RNNs outperform humans. Proc. Cognition, Artificial Life and Computational Intelligence , P. Sincak, V. Kvasnicka, J. Pospichal et al (Eds), May 15-17, Stara Lesna, Tatry, Slovakia, pp. 109-114. pdf

2002
  • Tino P, Cernansky M and Benuskova L (2002) Markovian architectural bias of recurrent neural networks. In: Intelligent Technologies - Theory and Applications. Frontiers in AI and Applications, vol. 76. P. Sincak, J. Vascak, V. Kvasnicka and J. Pospichal (Eds), IOS Press, Amsterdam, pp. 17-23. ISBN 1-58603-256-9. pdf
  • Benuskova L (2002) Kognitivna neuroveda. In: Rybar J, Benuskova L, Kvasnicka V (eds) Kognitivne vedy. Kalligram, Bratislava, pp. 47-104. ISBN 80-7149-515-8.
  • Jedlicka P, Benuskova L, Macakova J, Ostatnikova D (2002) Molekulove mechanizmy ucenia a pamati. In: Hulin I (ed) Patofyziologia, 6. vydanie. Slovak Academic Press (SAP), s.r.o, Bratislava, pp. 1183-1199. ISBN 80-8910-405-3.
  • Benuskova L (2002) Mapy nasich skusenosti (co tvaruje nas mozog). Quark 8(2): 21-23.
  • Micusik D, Stopjakova V, Benuskova L (2002) Application of feed-forward artificial neural networks to the identification of defective analog integrated circuits. Neural Computing and Applications 11(1): 71-79. ISSN 0941-0643. link
  • Stopjakova V, Micusik D, Benuskova L, Margala M (2002) Neural Networks-Based Parametric Testing of Analog IC. In: Proc. 17th IEEE Intl. Symposium on Defect and Fault-Tolerance in VLSI Systems, DFT 2002, (pp. 408-418) Vancouver, BC, Canada: IEEE Press. ISSN 1063-6722.

2001
  • Benuskova L, Rema V, Armstrong-James M and Ebner FF (2001) Theory for normal and impaired experience-dependent plasticity in neocortex of adult rats. Proc. Natl. Acad. Sci. USA 98(5): 2797-2802. ISSN 0027-8424. link
  • Benuskova L, Kanich M, Krakovska A (2001) Piriform cortex model of EEG has random underlying dynamics. In: Proc. World Congress on Neuroinformatics. F. Rattay (Ed), ARGESIM/ASIM-Verlag, Vienna, pp. 287-292.
  • Benuskova L (2001) How some characteristics of cortical frequency representation may influence our perception of sounds. In: Proc. Intl. Conf. Art. Neural Net. and Genet. Alg. - ICANNGA'2001. V. Kurkova, N.C. Steele, R. Neruda and M. Karny (Eds), Springer-Verlag, Wien, New York, pp. 130-133.
  • Cernansky M and Benuskova L (2001) Finite-state Reber automaton and the recurrent neural networks trained in supervised and unsupervised manner. In: Artificial Neural Networks - ICANN'2001, Lecture Notes in Computer Science, vol. 2130. G. Dorffner, H. Bischof and K. Hornik (Eds), Springer-Verlag, Berlin, Heidelberg, pp. 737-742. ISBN 3-540-42486-5. link
  • Benuskova L (2001) Neurovedne okno do vedomia. Spasmus 9(2): 6-8.

2000
  • Benuskova L (2000) The intra-spine electric force can drive vesicles for fusion: a theoretical model for long-term potentiation Neuroscience Letters 280(1): 17-20. ISSN 0304-3904. link
  • Tino P, Stancik M and Benuskova L (2000) Building predictive models on complex symbolic sequences with a second-order recurrent BCM network with lateral inhibition. In: Proc. IEEE-INNS-ENNS Intl. Joint Conference on Neural Networks, vol. 2, pp. 265-270. ISBN 0-7695-0619-4.
  • Tino P, Stancik M and Benuskova L (2000) Building predictive models on complex symbolic sequences via a first-order recurrent BCM network with lateral inhibition. In: Quo Vadis Computational Intelligence? New Trends and Approaches in Computational Intelligence. P. Sincak and J. Vascak (Eds), Physica-Verlag, Heidelberg, pp. 42-50.
  • Benuskova L (2000) Neurobiology keeps inspiring new neural network models. ERCIM News No. 43 - Oct 2000, pp. 39-40.
  • Benuskova L (2000) Vidiet znamena vediet: pamat neuronovych sieti. In: Hladanie spolocneho jazyka v kognitivnych vedach. Benuskova L, Kvasnicka V, Pospichal J (eds), Iris, Bratislava, pp. 11-26. ISBN 80-88778-13-1. (about this book)
  • Benuskova L (2000) Neuronove siete a vnimanie. Quark 6(9): 21-23.

Before 2000
  • Benuskova L, Ebner FF, Diamond ME and Armstrong-James M (1999) Computational study of experience-dependent plasticity in adult rat cortical barrel-column. Network: Computation in Neural Systems 10(4): 303-323. link
  • Poljovka S, Benuskova L (1999) Pattern classification with the BCM neural network. In: Proc. 2nd Electronic Circuits and Systems Conference - ECS'99 , V. Stopjakova (Ed), Bratislava, pp. 207-210.
  • Petrovic P, Tino P, Benuskova L (1998) Processing symbolic sequences by the BCM neuron. Neural Network World 8(5): 491-500.
  • Benuskova L and Estok S (1998) A hypothetical neural mechanism that may play a role in mental rotation: an attractor neural network model. Network: Computation in Neural Systems 9(4): 513-530. link
  • Kvasnicka V, Benuskova L, Pospichal J, Farkas I, Tino P, Kral A (1997) Uvod do teorie neuronovych sieti. Iris, Bratislava. (cela kniha, pdf.zip )
  • Benuskova L (1997) Modelling plasticity in rat barrel cortex induced by one spared whisker. In: Artificial Neural Networks - ICANN'97, Lecture Notes in Computer Science 1327. W.Gerstner, W.Germond, M.Hasler, J.-D.Nicaud (Eds), Springer-Verlag, Berlin, pp. 127-132.
  • Benuskova L (1995) Modelling transpositional invariancy of melody recognition with an attractor neural network. Network: Computation in Neural Systems 6(3): 313-331. link
  • Benuskova L (1995) On the role of inhibition in cortical plasticity: a computational study. In: Proc. ICANN'95, vol.2 , pp. 521-526.
  • Benuskova L, Diamond ME and Ebner FF (1994) Dynamic synaptic modification threshold: Computational model of experience-dependent plasticity in adult rat barrel cortex. Proc. Natl. Acad. Sci. USA 91(11): 4791-4795. link
  • Benuskova L (1994) Modelling of the effect of the missing fundamental with an attractor neural network. Network: Computation in Neural Systems 5(3): 333-349. link
  • Benuskova L (1991) Antidepressants and synaptic plasticity: a hypothesis. Medical Hypotheses 35(1): 17-22. link
  • Benuskova L (1988) Mechanizmy synaptickej plasticity. Ceskoslovenska fysiologie 37(5): 387-400.
  • Fedor P, Benuskova L, Jakes H and Majernik V (1982) An electrophoretic coupling mechanism between the efficiency modification of spine synapses and their stimulation. Studia Biophysica 92: 141-146.