Publications of the CNC Group

  • Pócoš Š., Bečková I., Farkaš I.: RecViT: Enhancing Vision Transformer with Top-Down Information Flow. In 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 2024.pdfbib
  • Takáč, M., Knott, M., Sagar, M.: AI that looks, but doesn't see. In Šašinka, Č., et al. (eds.), Proceeedings of Cognition and Artificial Life 2024, Flow, z.s.. 25-27. 2024.pdfbib
  • Fandl, M., Takáč, M.: Learning distributed representations in a model of associative memory. In Šašinka, Č., et al. (eds.), Proceeedings of Cognition and Artificial Life 2024, Flow, z.s.. 36-39. 2024.pdfbib
  • Alieva, D., Schenk, J., Takáč, M. (eds.): Umelá inteligencia: sociálne a etické problémy. Univerzita Komenského v Bratislave. 2024.pdfbib
  • Pecháč M., Chovanec M., Farkaš I.: Self-supervised network distillation: An effective approach to exploration in sparse reward environments. Neurocomputing 2024.pdfbib
  • Farkaš I., Lúčny A., Vavrečka M.: Approaches to generating arm movements in humanoid robot NICO. In Cognition and Artificial Life, Flow, z.s.. 57-58. 2024.pdfbib
  • Cibula M., Kerzel M., Farkaš I.: Learning Low-Level Causal Relations using a Simulated Robotic Arm. In International Conference on Artificial Neural Networks (ICANN), Springer. 285-298. 2024.pdfbib
  • Sagar, M., Henderson, A., Takac, M., Morrison, S., Knott, A., Moser, A., Yeh, W.-T., Pages, N., Jawed, K.: Deconstructing and reconstructing turn-taking in caregiver-infant interactions: a platform for embodied models of early cooperation. Journal of the Royal Society of New Zealand 53(1). 148-168. 2023. DOI: 10.1080/03036758.2022.2098781.pdfbib
  • Sagar, M., Moser, A., Henderson, A., Morrison, S., Pages, N., Nejati, A., Yeh, W.-T., Conder, J., Knott, A., Jawed, K., Takac, M.: A platform for holistic embodied models of infant cognition, and its use in a model of event processing. IEEE Transactions on Cognitive and Developmental Systems 15(4). 1916–1927 . 2023.pdfbib
  • Pecháč M., Chovanec M., Farkaš I.: Self-supervised network distillation: an effective approach to exploration in sparse reward environments. Technical report. . 2023. arXiv:2302.11563 [cs.AI].pdfbib
  • Lúčny A., Malinovská K., Farkaš I.: Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models. In Artificial Neural Networks and Machine Learning – ICANN, Springer Nature Switzerland AG. 471-482. 2023.pdfbib
  • Kovač L., Farkaš I.: Safe Reinforcement Learning in a Simulated Robotic Arm. In Artificial Neural Networks and Machine Learning – ICANN, Springer Nature Switzerland AG. 585-589. 2023.pdfbib
  • Farkaš I.: Dôveryhodnost’ výpočtových modelov v umelej inteligencii a robotike. In Kognícia a umelý život XXI, Smolenice, Vydavateľstvo UK v Bratislave. 28-29. 2023.pdfbib
  • Farkaš, I., Ballová Mikušková, E., Takáč, M., Malinovská, K., Fandl, M.: Kognícia a umelý život 2023. Univerzita Komenského v Bratislave. 2023.pdfbib
  • Takáč, M.: Čo chýba ChatGPT, aby rozumel, čo robí?. In Farkaš, I., Ballová Mikušková, E., Takáč, M., Malinovská, K., Fandl, M. (eds.), Kognícia a umelý život 2023, Univerzita Komenského v Bratislave. 74-75. 2023.pdfbib
  • Fandl, M., Takáč, M.: Dizajn nového učiaceho pravidla pre moderné Hopfieldove siete. In Farkaš, I., Ballová Mikušková, E., Takáč, M., Malinovská, K., Fandl, M. (eds.), Kognícia a umelý život 2023, Univerzita Komenského v Bratislave. 26-27. 2023.pdfbib
  • Kubík, J., Kyselica, D., Takáč, M.: Efficient fine-tuning of SlovakBERT with Epinet. In Brejová, B. et al. (eds.), ITAT 2023 Information Technologies – Applications and Theory 2023, Aachen. 96-101. 2023.pdfbib
  • Šuba, D., Šuppa, M., Kubík, J., Hamerlik, E., Takáč, M.: WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition. In Piskorski, J. et al. (eds.), Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023), Association for Computational Linguistics. 138-145. 2023.pdfbib
  • Hamerlik, E., Deb, M., Takáč, M.: Bi-Source Class Visualization: An Adversarial Neural Network-based Approach for Unbiased Class Visualization. In World Symposium on Digital Intelligence for Systems and Machines (DISA2023). 2023.bib
  • Herashchenko D., Farkaš I.: Appearance-based gaze estimation enhanced with synthetic images using deep neural networks. In IEEE Symposium Series on Computational Intelligence (SSCI). 129-134. 2023.pdfbib
  • Knott, A., Sagar, M., Takac, M.: The ethics of interaction with neurorobotic agents: a case study with BabyX. AI and Ethics 2(1). 115-128. 2022.pdfbib
  • Reinboth T., Farkaš I.: Ultimate grounding of abstract concepts: A graded account. Journal of Cognition, 5(1). 2022.pdfbib
  • Farkaš I., Cimrová B., Pócoš Š., Bečková I.: Pozornost’ ako biologicky inšpirovaný koncept pre vysvetlitel’né, robustné a efektı́vne strojové učenie. In Šejnová G., Vavrečka M., Hvorecký J. (eds.), Kognice a umělý život 2022, ČVUT v Praze. 34-38. 2022.pdfbib
  • Bečková I., Pócoš Š., Farkaš I.: Skúmanie vzdialenostı́ adverzariálnych vstupov k jednotlivým triedam v hlbokých neurónových siet’ach. In Šejnová G., Vavrečka M., Hvorecký J. (eds.), Kognice a umělý život 2022, ČVUT v Praze. 160-161. 2022.pdfbib
  • Pecháč M., Farkaš I.: Intrinsic motivation based on feature extractor distillation. In Šejnová G., Vavrečka M., Hvorecký J. (eds.), Kognice a umělý život 2022, ČVUT v Praze. 84-90. 2022.pdfbib
  • Takáč, M., Knott, A., Sagar, M.: Etické aspekty neurorobotických simulácií. In Šejnová, G., Vavrečka, M., Hvorecký, J. (eds.), Kognice a umělý život XX., České vysoké učení technické v Praze. 126-132. 2022.pdfbib
  • Fandl, M., Takáč, M.: Zvyšovanie efektivity trénovania a kapacity v atraktorovom neurálnom modeli asociatívnej pamäte. In Šejnová, G., Vavrečka, M., Hvorecký, J. (eds.), Kognice a umělý život XX., České vysoké učení technické v Praze. 2022.pdfbib
  • Malinovský, Ľ., Malinovská, K.: Neurónová sieť s násobiacou vrstvou. In Šejnová, G., Vavrečka, M., Hvorecký, J. (eds.), Kognice a umělý život XX, České vysoké učení technické v Praze. 79-83. 2022.bib
  • Malinovská K., Farkaš I., Harvanová J., Hoffmann M.: A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot. In IEEE International Conference on Development and Learning (ICDL). 2022.pdfbib
  • Pócoš Š., Bečková I., Farkaš I.: Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. In International Conference on Artificial Neural Networks (ICANN), Springer. 645-656. 2022.pdfbib
  • Cimrová B., Marko M., Farkaš I., Sobota B., Korečko Š., Rošťáková Z., Rosipal R.: Tréning kapacity vizuálnej pracovnej pamäti v prostredí virtuálnej reality. In Šejnová G., Vavrečka M., Hvorecký J. (eds.) (eds.), Kognice a umělý život XX, České vysoké učení technické v Praze. 23-27. 2022.pdfbib
  • Knott, A., Takac, M.: Roles for Event Representations in Sensorimotor Experience, Memory Formation, and Language Processing. Topics in Cognitive Science 13(1). 187-205. 2021. DOI: 10.1111/tops.12497.pdfbib
  • Mark Sagar, Alecia Moser, Annette Henderson, Sam Morrison, Nathan Pages, Alireza Nejati, Wan-Ting Yeh, Jonathan Conder, Alistair Knott, Khurram Jawed, Martin Takac: A platform for embodied models of infant cognition, and its use in a model of event perception. In 2021 IEEE International Conference on Development and Learning (ICDL). 1-7. 2021. doi: 10.1109/ICDL49984.2021.9515612.pdfbib
  • Holas J., Farkaš I.: Advances in Adaptive Skill Acquisition. In Artificial Neural Networks and Machine Learning - ICANN 2021, Springer Nature Switzerland AG. 650-661. 2021.pdfbib
  • Pecháč M., Farkaš I.: Intrinsic Motivation Model Based on Reward Gating. In Artificial Neural Networks and Machine Learning - ICANN 2021, Springer Nature Switzerland AG. 688-699. 2021.pdfbib
  • Malinovská K., Farkaš I.: Generative properties of Universal Bidirectional Activation-based Learning. In Artificial Neural Networks and Machine Learning - ICANN 2021, Springer Nature Switzerland AG. 80-83. 2021.pdfbib
  • Šejnová G., Mendrechová M., Otahal M., Sokovnin N., Farkaš I., Vavrečka M.: Reward redistribution for reinforcement learning of dynamic nonprehensile manipulation. In International Conference on Control, Automation and Robotics. 326-331. 2021.pdfbib
  • Pócoš Š., Bečková I., Kuzma T., Farkaš I.: Assessment of manifold unfolding in trained deep neural network classifiers. In Trustworthy AI - Integrating Learning, Optimization and Reasoning, Springer Nature Switzerland AG. 2021.pdfbib
  • Takac, M., Knott, A., Sagar, M.: SOM-based System for Sequence Chunking and Planning. In Farkaš, I., Masulli, P., Wermter, S. (eds.), Artificial Neural Networks and Machine Learning – ICANN 2020, Springer Nature Switzerland AG. 672-684. 2020.pdfbib
  • Takac, M., Knott, A., Sagar, M.: C-block: A system for learning motor plans with perceptual consequences. In 1st SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) workshop, ICDL 2020. 2.-3. Nov 2020.pdfbib
  • Takáč, M.: Myseľ ako objekt. Kalligram. 2020.pdfbib
  • Holas J., Farkaš I.: Adaptive skill acquisition in hierarchical reinforcement learning. In Artificial Neural Networks and Machine Learning - ICANN 2020, Springer Nature Switzerland AG. 383-394. 2020.pdfbib
  • Bečková I., Pócoš Š., Farkaš I.: Computational analysis of robustness in neural network classifiers. In Artificial Neural Networks and Machine Learning - ICANN 2020, Springer Nature Switzerland AG. 65-76. 2020.pdfbib
  • Farkaš I., Masulli P., Wermter S. (eds.): Artificial Neural Networks and Machine Learning - ICANN 2020, part I. Springer Nature Switzerland AG. 2020.pdfbib
  • Farkaš I., Masulli P., Wermter S. (eds.): Artificial Neural Networks and Machine Learning - ICANN 2020, part II. Springer Nature Switzerland AG. 2020.pdfbib
  • Rosipal R., Porubcová N., Barančok P. , Cimrová B., Farkaš I., Trejo L.J.: Effects of mirror-box therapy on modulation of sensorimotor EEG oscillatory rhythms: A single-case longitudinal study. Journal of Neurophysiology 2019.pdfbib
  • Pospı́chal J., Farkaš I., Pecháč M., Malinovská K.: Modeling self-organized emergence of perspective in/variant mirror neurons in a robotic system. In Proceedings of the 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2019.pdfbib
  • Gergeľ P., Farkaš I.: Echo state networks with artificial astrocytes and hebbian connections. In 15th International Work-Conference on Artificial Neural Networks, Springer. 457-466. 2019.pdfbib
  • Kuzma T., Farkaš I.: Embedding complexity of learned representations in neural networks. In 28th International Conference on Artificial Neural Networks (ICANN), vol. 2. 518-528. 2019.pdfbib
  • Fele, B and Takáč, M: A connectionist model of acquisition of noun phrases with syntactic bootstrapping. In Farkaš I. et al. (eds.), Kognícia a umelý život 2019, Univerzita Komenského v Bratislave. 31-32. 2019.pdfbib
  • Farkaš I., Takáč M, Gergeľ P., Tomko M. (eds.): Kognícia a umelý život XIX. Univerzita Komenského v Bratislave. 2019.pdfbib
  • Korečko Š., Sobota B., Hudák M., Farkaš I., Cimrová B., Vasiľ P., Trojčák D.: Experimental procedure for evaluation of visuospatial cognitive functions training in virtual reality. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISI). 643-652. 2019.pdfbib
  • Malinovská, K., Malinovský, Ľ., Krsek, P., Kraus, S., Farkaš, I.: UBAL: a Universal Bidirectional Activation-based Learning Rule for Neural Networks. In CIIS 2019: Proceedings of the 2019 International Conference on Computational Intelligence and Intelligent Systems, Association for Computing Machinery, New York, NY, USA. 2019.pdfbib
  • Takáč, M.: (Z)mení umelá inteligencia našu budúcnosť?. Kritika a kontext 57 2019.pdfbib
  • Šikudová E., Malinovská K., Škoviera R., Škovierová J., Uller M., Hlaváć V.: Estimating pedestrian intentions from trajectory data. In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE. 19-25. 2019.pdfbib
  • Kraus S., Krsek P., Malinovská K., Tuna M.: Detecting Wearable Objects via Transfer Learning. In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE. 373-380. 2019.pdfbib
  • Hoffmann M., Straka Z., Farkaš I., Vavrečka M., Metta G.: Robotic homunculus: Learning of artificial skin representation in a humanoid robot motivated by primary somatosensory cortex. IEEE Transactions on Cognitive and Developmental Systems 10(2). 163-176. 2018.pdfbib
  • Jug J., Kolenik T., Ofner A., Farkaš I.: Computational model of enactive visuospatial mental imagery using saccadic perceptual actions. Cognitive Systems Research 49, 157-177. 2018.pdfbib
  • Malinovská, K., Malinovský, Ľ, Farkaš, I.: UBAL: Univerzálny biologicky-motivovaný algoritmus s lokálnym pravidlom. In Šašinka et al. (eds.), Kognice a umělý život XVIII, Flow, z.s., Brno. 50-52. 2018.pdfbib
  • Tuna, M, Malinovská, K., Farkaš, I.: Kategorické Siamské siete. In Šašinka et al. (eds.), Kognice a umělý život XVIII, Flow, z.s., Brno. 80-81. 2018.bib
  • Kuzma T., Farkaš I.: Computational analysis of learned representations in deep neural network classifiers. In International Joint Conference on Neural Networks, IEEE, Rio de Janeiro. 4420-4425. 2018.pdfbib
  • Torda M,, Farkaš I.: Evaluation of information-theoretic measures in echo state networks on the edge of stability. In International Joint Conference on Neural Networks, IEEE, Rio de Janeiro. 117-122. 2018.pdfbib
  • Korečko Š., Hudák M., Sobota S., Marko M., Cimrová B., Farkaš I., Rosipal R.: Assessment and training of visuospatial cognitive functions in virtual reality: proposal and perspective. In Proceedings of 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) . 39-43. 2018.pdfbib
  • Cimrová B., Rošťáková Z., Varga Doležalová M., Farkaš I., Rosipal R.: Hľadanie súvislostí medzi charakteristikami spánku a kognitívnym výkonom pacientov s ložiskovou ischémiou mozgu. In Kognice a umělý život XVIII, Flow, z.s. , Brno. 11-12. 2018.pdfbib
  • Gergeľ P. and Farkaš I.: Investigating the role of astrocyte units in a feedforward neural network. In International Conference on Artificial Neural Networks. 73--83. 2018.pdfbib
  • Jug J., Kolenik T., Ofner A., Farkaš I.: Výpočtový model enaktívnej vizuálno-priestorovej mentálnej predstavivosti. In Šašinka et al. (eds.), Kognice a umělý život XVIII, Flow, z.s.. 31-32. 2018.bib
  • Holas J.: Adaptive Skill Acquisition in Hierarchical Reinforcement Learning. In Šašinka et al. (eds.) (eds.), Kognice a umělý život XVIII, Flow, z.s.. 25-26. 2018.bib
  • Takáč, M: Modelovanie hipokampálnej reprezentácie priestoru pomocou rekurentnej samoorganizujúcej sa mapy. In Kognice a umělý život, Flow, Brno. 76-77. 2018.pdfbib
  • Takac, M and Knott, A: A neural network model of event representations: Sensorimotor sequencing, place coding, self-organization, and Bayesian inference. In Computational Neuroscience of Event Cognition (CONNECT), University of Otago, Dunedin. 2018.pdfbib
  • Malinovská K., Malinovský Ľ., Farkaš I.: Towards more biologically plausible error-driven learning for artificial neural networks. In Artificial Neural Networks and Machine Learning (ICANN 2018), Springer. 228-231. 2018.pdfbib
  • Takac, M. and Knott, A. and Stokes, S.: What can Neighbourhood Density effects tell us about word learning? Insights from a connectionist model of vocabulary development. Journal of Child Language 44(2). 346-379. 2017.pdfbib
  • Patrzyk, P. and Takac, M.: Cognitive adaptations to criminal justice lead to “paranoid” norm obedience. Adaptive Behavior 25(2). 83-95. 2017.pdfbib
  • Knott, A., Szymanski, L., McCane, B., Takac, M.: A model of object property representations: visual object classification, working memory and the syntax of predication. Technical report OUCS-2017-03. Department of Computer Science, University of Otago, Dunedin, New Zealand. 2017.pdfbib
  • Farkaš I., Gergeľ P.: Maximizing memory capacity of echo state networks with orthogonalized reservoirs. In International Joint Conference on Neural Networks, IEEE. 2437-2442. 2017.pdfbib
  • Gergeľ, P.: Improving the performance of impulse neuro–glial network. In Kognícia a umelý život 2017, Vydavateľstvo Univerzity Komenského, Bratislava. 60-63. 2017.pdfbib
  • Patrzyk, P. and Takac, M.: Cooperation Via Intimidation: An Emergent System of Mutual Threats Can Maintain Social Order. Journal of Artificial Societies and Social Simulation (JASSS) 20(4). 2017. DOI: 10.18564/jasss.3336.pdfbib
  • Knott, A and Takac, M: A sensorimotor interpretation of Logical Form, and its application in a model of Māori sentences. In Quinn, H and Massam D and Matthewson L (eds.), Linguistic travels in time and space: Festschrift for Liz Pearce. Wellington Working Papers in Linguistics Volume 23. 101-114. 2017.pdfbib
  • Farkaš, I., Takáč, M., Rybár, J., Gergeľ, P.: Kognícia a umelý život 2017. Univerzita Komenského v Bratislave, Bratislava. 2017.pdfbib
  • Takac, M and Knott, A: A simulationist model of episode representations in working memory. Technical report. University of Otago. 2016. TR OUCS-2016-01.pdfbib
  • Palkovics, M.A. and Takac, M.: Exploration of cognition - affect and Type 1 - Type 2 dichotomies in a computational model of decision making. Cognitive Systems Research 40, 144-160. 2016.pdfbib
  • Farkaš I.: On abstraction: psychological, neural, and computational perspectives. In Kognice a umělý život (KUZ). 2016.pdfbib
  • Rybár J., Cimrová B., Farkaš I., Varga Doležalová M., Rosipal R.: Špecifiká kognitívneho výkonu pacientov po ložiskovej ischémii mozgu. In Kognice a umělý život (KUZ). 2016.pdfbib
  • Takac, M. and Knott, A.: Working memory encoding of events and their participants: a neural network model with applications in sensorimotor processing and sentence generation. In Papafragou, A. and Grodner, D. and Mirman, D. and Trueswell, J.C. (eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX. 2345-2350. 2016.pdfbib
  • Takac, M and Knott, A: Mechanisms for storing and accessing event representations in episodic memory, and their expression in language: a neural network model. In Papafragou, A. and Grodner, D. and Mirman, D. and Trueswell, J.C. (eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX. 532-537. 2016.pdfbib
  • Farkaš I., Bosák R., Gergeľ P.: Computational analysis of memory capacity in echo state networks. Neural Networks 83, 109-120. 2016.pdfbib
  • Knott, A and Takac, M: A simulationist model of episode representations in working memory: syntactic interpretation, nested episodes and storage requirements. Technical report. University of Otago. 2016. OUCS-2016-04.pdfbib
  • Csiba P., and Farkaš I.: Computational analysis of the bidirectional activation-based learning in autoencoder task. In The International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. 2015.pdfbib
  • Rosipal R., Cimrová B., Rückschloss L., Kohút Z., Farkaš I., Porubcová N.: Vplyv zrkadlového tréningu na moduláciu motorických rytmov: elektrofyziologická a klinická štúdia pacienta s hemiparézou po mozgovom infarkte. In Kognícia a umelý život XV, Vydavateľstvo Univerzity Komenského, Bratislava. 152-157. 2015.pdfbib
  • Kuzma T., Farkaš I.: Automatická tvorba učebných plánov pre strojové učenie. In Kognícia a umelý život XV, Vydavateľstvo Univerzity Komenského, Bratislava. 99-102. 2015.pdfbib
  • Kundlová, V, Malinovský, Ľ, Rebrová, K, Sedláček, M.: Emergencia komunikácie v laboratóriu: triadická interakcia. In Farkaš, I et al. (eds.), Kognícia a umelý život 2015, Vydavateľstvo Univerzity Komenského. 91-97. 2015.bib
  • Knott, A., Benuskova, L., Takac, M.: A model of semantic working memory: representing episodes and individuals as prepared sensorimotor sequences. In NeuroEng 2015: 8th Australasian Workshop on Computational Neuroscience, Queenstown, New Zealand 26 – 28 August. 2015.pdfbib
  • Farkaš, I., Takáč, M., Rybár, J., Kelemen, J. (eds.): Kognícia a umelý život XV. Univerzita Komenského v Bratislave, Bratislava. 2015.pdfbib
  • Takáč, M., Knott. A.: Reprezentácia epizód v pracovnej pamäti, problém viazania a vytváranie očakávaní. In Farkaš, I., Takáč, M., Rybár, J., Kelemen, J. (eds.), Kognícia a umelý život 2015, Univerzita Komenského v Bratislave, Bratislava. 178-184. 2015.pdfbib
  • Takáč, M., Knott. A.: A neural network model of episode representations in working memory. Cognitive Computation 7(5). 509-525. 2015.pdfbib
  • Gergeľ P., Farkaš I.: Connectionist modeling of part–whole analogy learning. In Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science. 587-592. 2015.pdfbib
  • Takac, M and Knott, A: A simulationist model of episode representations in working memory:Technical appendix. Technical report OUCS-2015-01. Department of Computer Science, University of Otago. 2015.pdfbib
  • Knott, A and Szymanski, L and Gorman, C and Takac, M: Predicative sentences and perceptual mechanisms. In Proceedings of Linguistic Society of New Zealand Conference, Dunedin, New Zealand, 14-15 December. 2015.pdfbib
  • Knott, A and Takac, M: Training a neural network sentence generator to produce Māori sentences. In Proceedings of Linguistic Society of New Zealand Conference, Dunedin, New Zealand, 14-15 December. 2015.pdfbib
  • Vavrečka M., Farkaš I.: A multimodal connectionist architecture for unsupervised grounding of spatial language. Cognitive Computation 6:101-112. 2014.pdfbib
  • Rebrová, K.: Grounding the meaning in sensorimotor cognition: a connectionist approach (dissertation thesis). Comenius University in Bratislava. 2014.pdfbib
  • Farkaš I., Cimrová B., Rybár J.: Potlačenie motorických rytmov v mozgu pri pozorovaní pohybu. In Kognitivní věda a umělý život II, Slezská univerzita v Opave. 67-74. 2014.pdfbib
  • Cimrová B., Farkaš I., Rosipal R.: Využitie rozhrania mozog–počítač pri neurorehabilitácii: prehľad aktuálneho výskumu. In Kognitivní věda a umělý život II, Slezská univerzita v Opave. 33-38. 2014.pdfbib
  • Švec M., Farkaš I.: Calculation of object position in various reference frames with a robotic simulator. In Proceedings of the 36th Annual Conference of the Cognitive Science Society . 2014.pdfbib
  • Barančok P., Farkaš I.: Memory capacity of input-driven echo state networks at the edge of chaos. In Proceedings of the International Conference on Artificial Neural Networks (ICANN). 41-48. 2014.pdfbib
  • Takáč, M.: Modelovanie efektu fonologického susedstva v akvizícii jazyka pomocou jednoduchej rekurentnej siete. In Kelemen, J., Vavrečková, Š. (eds.), , Slezská univerzita, Opava. 205-210. 2014.pdfbib
  • Takáč, M., Knott, A.: A revised neural network model of episode representations in working memory. Technical report OUCS-2014-03. Department of Computer Science, University of Otago. 2014.pdfbib
  • Gergeľ, P.: Konekcionistické modelovanie učenia sa analógií. In Študentská vedecká konferencia FMFI UK. 60-68. 2014.pdfbib
  • Takac, M., Knott, A.: A neural network model of working memory for episodes. Technical report OUCS-2013-01. Department of Computer Science, University of Otago. 2013.pdfbib
  • Wein, A., Takáč, M.: Sensorimotor Characterization of Semantic Structures. Technical report TR-2013-037. Faculty of Mathematics, Physics, and Informatics Comenius University, Bratislava. 2013.pdfbib
  • Pileckyte, I., Takáč, M.: Computational model of intrinsic and extrinsic motivation for decision making and action-selection. Technical report TR-2013-038. Faculty of Mathematics, Physics, and Informatics Comenius University, Bratislava. 2013.pdfbib
  • Anetta, K., Takáč, M.: Towards Advanced Text Quality Evaluation Modelling. Technical report TR-2013-036. Faculty of Mathematics, Physics, and Informatics Comenius University, Bratislava. 2013.pdfbib
  • Malinovský Ľ.: Výpočtový model zavádzania ako spoločnej akcie. In Kelemen J. et al. (ed.) (eds.), Kognitivní věda a umělý život, Slezská univerzita, Opava. 157-166. 2013.bib
  • Farkaš I.: Je reprezentačný pluralizmus v kognitívnej vede nevyhnutný?. In Kelemen J., Rybár J., Farkaš I., Takáč M. (eds.), Kognitivní věda a umělý život, Slezská univerzita v Opavě. 107-114. 2013.pdfbib
  • Rebrová K., Farkaš I.: Robotický model systému zrkadliacich neurónov: experimentálna analýza. In Kelemen J., Rybár J., Farkaš I., Takáč M. (eds.), Kognitivní věda a umělý život, Slezská univerzita v Opavě. 223-230. 2013.pdfbib
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