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. link
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.
Lecture Notes in Computer Science, vol 12397, pp 670-680. Springer, Cham.
link
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.
link
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. link
Shirrafiardekani A, Benuskova L,
Frauendiener J (2019) A voltage-based metaplasticity rule applied
to the model hippocampal granule cell accounts for homeostatic heterosynaptic plasticity.
BioRxiv: doi: https://doi.org/10.1101/557173.
link
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
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.
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
Takac M,
Knott A, Benuskova L (2012) Mapping sensorimotor sequences to word sequences:
A connectionist model of language acquisition and sentence generation.
Cognition 125(2): 288-308.
link
(Supplementary material
TR OUCS-2011-03 )
Benuskova L,
Jedlicka P (2012) Computational modeling of long-term depression
of synaptic weights: insights from STDP, metaplasticity and spontaneous
activity. Neural Network World 22(2): 161-180. ISSN 1210-0552.
pdf
Benuskova L (2012) Why is it hard to induce LTD?
Proc. International Joint Conference on Neural Networks (IJCNN 2012), pp. 3329-3335.
Nido GS, Williams JM, Benuskova L (2012)
Bistable properties of a memory-related gene regulatory network.
Proc. International Joint Conference on Neural Networks (IJCNN 2012), pp. 1602-1607.
Blanchette G, O'Keefe R, Benuskova L (2012)
Inference of a phylogenetic tree: hierarchical clustering versus genetic algorithm.
In: M. Thielscher and D. Zhang (Eds.) AI 2012, LNCS 7691, Springer-Verlag,
Berlin / Heidelberg, pp. 300-312.
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
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
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
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
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.
link
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.
link
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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) 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.
Benuskova L (2000) Neuronove siete a vnimanie. Quark
6(9): 21-23.
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.
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.