Cognition and Neural Computation Group

Research Interests of the CNC Group

Cognitive developmental robotics

The focus is on modeling cognitive development of an embodied agent (using the iCub simulator). The subgoals include sensorimotor coordination, learning multimodal representations, and basic language skills, thereby grounding the linguistic meanings.

Related publications:

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.
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.
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.
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.

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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.
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.
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.
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.
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.
Rebrová, K.: Grounding the meaning in sensorimotor cognition: a connectionist approach (dissertation thesis). Comenius University in Bratislava. 2014.
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.
Rebrová K., Pecháč M., Farkaš I.: Towards a robotic model of the mirror neuron system. In International Conference on Development and Learning and on Epigenetic Robotics, IEEE. 2013.
Farkaš I., Malík T., Rebrová K.: Grounding the meanings in sensorimotor behavior using reinforcement learning. Frontiers in Neurorobotics 6(1). 2012. doi: 10.3389/fnbot.2012.00001.
Rebrová K., Farkaš I.: Neurálne modely v kognitívnej robotike: porozumenie a pomenovávanie akcií. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život XI, Slezská univerzita, Opava. 231-238. 2011.
Farkaš I., Malík T.: Connectionist model of action learning and naming (abstract). In IEEE ICDL-EpiRob 2011: IEEE Conference on Development and Learning and Epigenetic Robotics , Frankfurt am Main. 2011.
Malinovský, Ľ: Universal data categorizator architecture. In Racsmány M. et al. (ed.) (eds.), Language and Perception 1 Suppl., Akadémiai Kiadó, Budapest. 29. 2009.

visuo-motor coordination

Related publications:

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.
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.
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.
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.

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Takáč, M., Knott. A.: A neural network model of episode representations in working memory. Cognitive Computation 7(5). 509-525. 2015.
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.
Rebrová, K.: Grounding the meaning in sensorimotor cognition: a connectionist approach (dissertation thesis). Comenius University in Bratislava. 2014.
Wein, A., Takáč, M.: Sensorimotor Characterization of Semantic Structures. Technical report TR-2013-037. Faculty of Mathematics, Physics, and Informatics Comenius University, Bratislava. 2013.
Farkaš I., Malík T., Rebrová K.: Grounding the meanings in sensorimotor behavior using reinforcement learning. Frontiers in Neurorobotics 6(1). 2012. doi: 10.3389/fnbot.2012.00001.
Rebrová K., Farkaš I.: Neurálne modely v kognitívnej robotike: porozumenie a pomenovávanie akcií. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život XI, Slezská univerzita, Opava. 231-238. 2011.
Farkaš I.: Neurálne modely v kognitívnej robotike: vizuálno-motorická interakcia. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život X, Slezská univerzita, Opava. 93-99. 2010.
Takáč, M.: Categorization by Sensory-Motor Interaction in Artificial Agents. In Fum, D., Del Missier, F., Stocco, A. (eds.), Proceedings of the 7th International Conference on Cognitive Modeling, Edizioni Goliardiche, Trieste, Italy. 310-315. 2006.

mind-body problem (causality)

Related publications:

Reinboth T., Farkaš I.: Ultimate grounding of abstract concepts: A graded account. Journal of Cognition, 5(1). 2022.
Farkaš I.: Mental causation in a physical brain?. In Brain-Inspired Cognitive Systems, Universidad Politécnica de Madrid. 14-16 July 2010.
Farkaš I.: Hľadanie kauzálnych vzťahov v probléme mysle a tela z pohľadu neredukcionistického fyzikalizmu. In V. Kvasnička, P. Trebatický, J. Pospichal, J. Kelemen (eds.), Myseľ, inteligencia a život, Vydavateľstvo STU, Bratislava. 3-16. 2007.
Farkaš I.: Recipročná kauzalita medzi mysľou a mozgom z neurovednej perspektívy. In J. Kelemen, V. Kvasnicka, J. Pospichal (eds.), Kognícia a umelý život V, Smolenice, Slovakia. 117-124. 2005.

understanding observed behavior (mirror neuron system, theory of mind, neural correlates of action)

Related publications:

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.
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.
Rebrová, K.: Grounding the meaning in sensorimotor cognition: a connectionist approach (dissertation thesis). Comenius University in Bratislava. 2014.
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.

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Rebrová K., Pecháč M., Farkaš I.: Towards a robotic model of the mirror neuron system. In International Conference on Development and Learning and on Epigenetic Robotics, IEEE. 2013.
Rebrová K.: Stelesnené porozumenie a ideomotorická teória. In Rybár, J. (eds.), Kognitívne paradigmy, Vydavateľstvo Európa. 127-150. 2012.
Rebrová K., Farkaš I.: Robotický model systému zrkadliacich neurónov. In Kelemen J., Nahodil P. (eds.), Kognice a umělý život XII, Slezská univerzita. 231-238. 2012.
Farkaš I., Malý M., Rebrová K.: Porozumenie motorickým akciám – hypotéza kontinua. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život XI, Slezská univerzita. 61-68. 2011.
Farkaš I., Malý M., Rebrová K.: Mirror neurons – theoretical and computational issues. Technical report TR-2011-028. Comenius University in Bratislava. 2011.
Farkaš I.: Neurálne modely v kognitívnej robotike: vizuálno-motorická interakcia. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život X, Slezská univerzita, Opava. 93-99. 2010.

Connectionist systematicity

Related publications:

Farkaš I., Pokorný M.: Investigating systematicity in the linear RAAM neural network. In Mayor J., Ruh N., Plunkett K. (eds.), Connectionist Models of Behaviour and Cognition, Vol. II, World Scientific, Singapore. 217-228. 2009.
Farkaš I., Crocker M.: Syntactic systematicity in sentence processing with a recurrent self-organizing network. Neurocomputing 711172-1179. 2008.
Farkaš I., Crocker M.: Systematicity in sentence processing with a recursive self-organizing neural network. In M. Verleysen (eds.), Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN'07), Bruges, Belgium. 49-54. 2007.

Properties of Artificial Neural Networks

Related publications:

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.
Fandl, M., Takáč, M.: Learning distributed representations in a model of associative memory. In Šašinka, Č., et al. (eds.), Proceedings of Cognition and Artificial Life 2024, Flow, z.s.. 36-39. 2024.
Fandl, M., Takáč, M.: Towards a Model of Associative Memory with Learned Distributed Representations. In Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds.), Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15016, Springer, Cham.. 226-241. 2024.
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.

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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.
Hamerlik, E., Deb, M., Takáč, M.: Bi-Source Class Visualization: An Adversarial Neural Network-based Approach for Unbiased Class Visualization. In Sinčák, P., Magyar, J., Szabóová, M. (eds.), World Symposium on Digital Intelligence for Systems and Machines (DISA2023), Institute of Electrical and Electronics Engineers, New York, USA. 2023.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Farkaš I., Masulli P., Wermter S. (eds.): Artificial Neural Networks and Machine Learning - ICANN 2020, part I. Springer Nature Switzerland AG. 2020.
Farkaš I., Masulli P., Wermter S. (eds.): Artificial Neural Networks and Machine Learning - ICANN 2020, part II. Springer Nature Switzerland AG. 2020.
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.
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.
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.
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.
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.
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.
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.
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.
Takáč, M: Modelovanie hipokampálnej reprezentácie priestoru pomocou rekurentnej samoorganizujúcej sa mapy. In Kognice a umělý život, Flow, Brno. 76-77. 2018.
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.
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.
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.
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.
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.
Farkaš I., Bosák R., Gergeľ P.: Computational analysis of memory capacity in echo state networks. Neural Networks 83, 109-120. 2016.
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.
Kuzma T., Farkaš I.: Automatická tvorba učebných plánov pre strojové učenie. In Kognícia a umelý život XV, Vydavateľstvo Univerzity Komenského, Bratislava. 99-102. 2015.
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.
Takáč, M., Knott. A.: A neural network model of episode representations in working memory. Cognitive Computation 7(5). 509-525. 2015.
Gergeľ P., Farkaš I.: Connectionist modeling of part–whole analogy learning. In Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science. 587-592. 2015.
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.
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.
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.
Vavrečka M., Farkaš I.: A multimodal connectionist architecture for unsupervised grounding of spatial language. Cognitive Computation 6:101-112. 2014.
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.
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.
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.
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.
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.
Farkaš I., Rebrová K.: Bidirectional activation-based neural network learning algorithm. In Proceedings of the International Conference on Artificial Neural Networks (ICANN), Springer. 154-161. 2013.
Rebrová K., Pecháč M., Farkaš I.: Towards a robotic model of the mirror neuron system. In International Conference on Development and Learning and on Epigenetic Robotics, IEEE. 2013.
Takáč, M., Knott, A.: A neural network model of working memory for episodes. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX. 1432-1437. 2013.
Takáč, M., Knott, A.: Konekcionistický model epizodickej pracovnej pamäti. In Kelemen, J., Rybár, J., Farkaš, I., Takáč, M. (eds.), Kognitívní věda a umělý život, Slezská univerzita, Opava. 265-272. 2013.
Farkaš I., Malík T., Rebrová K.: Grounding the meanings in sensorimotor behavior using reinforcement learning. Frontiers in Neurorobotics 6(1). 2012. doi: 10.3389/fnbot.2012.00001.
Farkaš I.: Indispensability of computational modeling in cognitive science. Journal of Cognitive Science 13,401-435. 2012.
Švantner J., Farkaš I., Crocker M.: Modeling utterance-mediated attention in situated language comprehension. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, US. 2235-2240. 2011.
Vavrečka M., Farkaš I.: Unsupervised grounding of spatial relations. In Kokinov B., Karmiloff-Smith A., and Nersessian N. (eds.), Proceedings of the European Conference on Cognitive Science, Sofia, New Bulgarian University Press, Sofia. 2011.
Rebrová K., Farkaš I.: Neurálne modely v kognitívnej robotike: porozumenie a pomenovávanie akcií. In Kelemen J., Kvasnička V. (eds.), Kognice a umělý život XI, Slezská univerzita, Opava. 231-238. 2011.
Farkaš I., Malý M., Rebrová K.: Mirror neurons – theoretical and computational issues. Technical report TR-2011-028. Comenius University in Bratislava. 2011.
Vančo P., Farkaš I.: Experimental comparison of recursive self-organizing maps for processing tree-structured data. Neurocomputing 73 1362-1375. 2010.
Vančo P., Farkaš I.: Recursive self-organizing networks for processing tree structures: Empirical comparison. In International Joint Conference on Computational Intelligence (IJCCI), Madeira, Portugal. 459-466. 2009.
Farkaš I.: Learning nonadjacent dependencies with a recurrent neural network. In M. Koeppen et al. (eds.), International Conference on Neural Information Processing Systems (ICONIP'2008), Springer, Lecture Notes in Computer Science. 292-299. 2009.
Farkaš I., Pokorný M.: Investigating systematicity in the linear RAAM neural network. In Mayor J., Ruh N., Plunkett K. (eds.), Connectionist Models of Behaviour and Cognition, Vol. II, World Scientific, Singapore. 217-228. 2009.
Švantner J., Farkaš I.: Učenie gramatických závislostí pomocou neurónovej siete s echo stavmi. In Kognícia a umelý život IX, Slezská univerzita, Opava. 319-325. 2009.
Farkaš I., Crocker M.: Syntactic systematicity in sentence processing with a recurrent self-organizing network. Neurocomputing 711172-1179. 2008.
Farkaš I., Crocker M.: Systematicity in sentence processing with a recursive self-organizing neural network. In M. Verleysen (eds.), Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN'07), Bruges, Belgium. 49-54. 2007.
Farkaš I., Švantner J.: Učenie nesusedných závislostí pomocou rekurentných neurónových sietí. In Kognícia a umelý život VII, Slezská univerzita, Opava. 389-394. 2007.
Farkaš I., Crocker M.:: Recurrent networks and natural language: exploiting self-organization. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, Vancouver, Canada. 1275-1280. 2006.
Tiňo P., Farkaš I., van Mourik J.: Dynamics and topographic organization of recursive self-organizing maps. Neural Computation 182529-2567. 2006.
Tiňo P., Farkaš I., van Mourik J.: Recursive self-organizing map as a contractive iterative function system. In M. Gallagher, J. Hogan, F. Maire (eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2005, Lecture Notes in Computer Science, Springer. 327-334. 2005.
Tiňo P., Farkaš I.: On non-Markovian topographic organization of receptive fields in recursive self-organizing map. In L. Wang, K. Chen, Y.S. Ong. (eds.), Advances in Natural Computation - ICNC 2005, Lecture Notes in Computer Science, Springer. 676-685. 2005.
Farkaš I.: Konekcionistické modelovanie jazyka. In J. Rybar, V. Kvasnicka, I. Farkas (eds.), Jazyk a kognícia, Kalligram, Bratislava. 262-305. 2005.
Li P., Farkaš I., MacWhinney B.: Early lexical development in a self-organizing neural network. Neural Networks 17(8-9). 1345-1362. 2004.
Farkaš I.: Lexical acquisition and developing semantic map. Neural Network World 13(3). 235-245. 2003.
Farkaš I., Li P.: Modeling the development of lexicon with a growing self-organizing map. In Caulfield, H.J. et al. (eds.), Proceedings of the 6th Joint Conference on Information Sciences, Research Triangle Park, NC. 553-556. 2002.
Li P., Farkaš I.: A self-organizing connectionist model of bilingual processing. In R. Heredia & J. Altarriba (eds.), Bilingual Sentence Processing, North-Holland: Elsevier Science Publisher. 59-85. 2002.
Farkaš I., Li P.: DevLex: A self-organizing neural network model of the development of lexicon. In International Conference on Neural Information Processing (ICONIP), Singapore. 2002.
Farkaš I., Li P.: A self-organizing neural network model of the acquisition of word meaning. In E. Altmann, A. Cleeremans, C. Schunn, & W. Gray (eds.), Proceedings of the 4th International Conference on Cognitive Modeling, Fairfax, VA. 67-72. 2001.
Chudý L., Farkaš I.: Regionálna analýza pomocou samoorganizujúcich sa máp. Politická ekonomie 48(5). 685-697. 2000.
Chudý L., Farkaš I.: Prediction of chaotic time-series using dynamic cell structures and local linear models. Neural Network World 8(5). 481-489. 1998.
Rosipal R., Koska M., Farkaš I.: Prediction of chaotic time-series with a resource-allocating RBF network. Neural Processing Letters 71-13. 1998.
Farkaš I.: Invariance of Gaussian-vector mapping using a self-organizing map. Neural Network World 7(2). 153-159. 1997.
Farkaš I.: Samoorganizujúce sa mapy. In V. Kvasnicka, L. Benuskova, J. Pospichal, I. Farkas, P. Tino, A. Kral (eds.), Úvod do teórie neurónových sietí, IRIS, Bratislava. 142-189. 1997.
Farkaš I., Chudý L.: Application of a growing self-organizing map to thinning of binary characters with noise. In Proceedings of WSOM'97: Workshop on Self-Organizing Maps, Espoo, Finland. 215-219. 1997.
Rosipal R., Koska M., Farkaš I.: Chaotic time-series prediction using resource-allocating RBF networks. In Frollo, I. and Plackova, A. (eds.), Measurement'97, Smolenice, Slovakia, Polygraphy of SAS Bratislava. 282-285. 1997.
Farkaš I.: Self-organized formant mapping. Neural Network World 5(3). 287-297. 1995.
Farkaš I.: Interference cancelling using constrained topological mapping. Neural Network World 3(2). 121-130. 1993.

Language acquisition and processing

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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.
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.
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.
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.

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Takac, M and Knott, A: A simulationist model of episode representations in working memory. Technical report. University of Otago. 2016. TR OUCS-2016-01.
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.
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.
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.
Vavrečka M., Farkaš I.: A multimodal connectionist architecture for unsupervised grounding of spatial language. Cognitive Computation 6:101-112. 2014.
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.
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.
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.
Takáč, M., Knott, A.: A neural network model of working memory for episodes. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX. 1432-1437. 2013.
Takáč, M., Knott, A.: Konekcionistický model epizodickej pracovnej pamäti. In Kelemen, J., Rybár, J., Farkaš, I., Takáč, M. (eds.), Kognitívní věda a umělý život, Slezská univerzita, Opava. 265-272. 2013.
Farkaš I., Malík T., Rebrová K.: Grounding the meanings in sensorimotor behavior using reinforcement learning. Frontiers in Neurorobotics 6(1). 2012. doi: 10.3389/fnbot.2012.00001.
Švantner J., Farkaš I., Crocker M.: Modeling utterance-mediated visual attention during situated comprehension. Neural Network World 22(2). 85-101. 2012.
Takáč, M., Beňušková, L., Knott, A.: Výpočtový model osvojovania abstraktnej a povrchovej syntaxe.. In Kelemen, J., Nahodil, P. (eds.), Kognice a umělý život XII, České vysoké učení technické, Praha. 220-225. 2012.
Takáč, M., Beňušková, L., Knott, A.: Language learning with meanings as stored sensorimotor sequences: a connectionist model. In Embodied and Situated Language Processing. 2012. Newcastle upon Tyne, UK.
Takac, M., Benuskova, L., Knott, A.: Mapping sensorimotor sequences to word sequences: A connectionist model of language acquisition and sentence generation. Cognition 125(2). 288-308. 2012.
Takáč, M.: Jazykové univerzáliá a stelesnená kognícia. In Rybár, J. et al (eds.), Kognitívne paradigmy, Europa. 151-166. 2012.
Takac, M., Benuskova, L., Knott, A.: A Sentence Generation Network that Learns Surface and Abstract Syntactic Structures. In Honkela, T.; Duch, W.; Girolami, M.; Kaski, S. (eds.), Artificial Neural Networks and Machine Learning - ICANN 2011, Springer, Heidelberg. 342-348. 2011.
Takac, M., Benuskova, L., Knott, A.: Mapping sensorimotor sequences to word sequences: A connectionist model of language acquisition and sentence generation. Technical report OUCS-2011-01. Department of Computer Science, University of Otago. 2011.
Takac, M., Benuskova, L., Knott, A.: A connectionist model of language acquisition and sentence generation: Technical appendix. Technical report OUCS-2011-03. Department of Computer Science, University of Otago. 2011.
Takac, M., Knott A., Benuskova, L.: Episode representations as stored sensorimotor sequences: a case study in sentence generation. In 4th Australian Workshop on Computational Neuroscience. 4-5 November 2010.
Takac, M., Knott, A., Benuskova, L.: Generation of idioms in a simple recurrent network architecture. Technical report OUCS-2010-02. Department of Computer Science, University of Otago. 2010.
Takac, M., Benuskova, L., Knott, A.: Sure as Eggs is Eggs: Low Entropy as Predictor of Idiomaticity in Language Production by a Simple Recurrent Network. In The 17th Australian Language and Speech Conference (ALAS’09), University of West Sydney, Sydney. 145. 2009.
Takac, M., Knott, A., Benuskova, L.: The Production of Idiomatic Language in a Simple Recurrent Network. In The 4th Computational Cognitive Neuroscience Conference (CCNC-09), Psychonomic Society, Green Valley. 53. 2009.
Švantner J., Farkaš I.: Učenie gramatických závislostí pomocou neurónovej siete s echo stavmi. In Kognícia a umelý život IX, Slezská univerzita, Opava. 319-325. 2009.
Farkaš I., Crocker M.:: Recurrent networks and natural language: exploiting self-organization. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, Vancouver, Canada. 1275-1280. 2006.
Farkaš I.: Konekcionistické modelovanie jazyka. In J. Rybar, V. Kvasnicka, I. Farkas (eds.), Jazyk a kognícia, Kalligram, Bratislava. 262-305. 2005.
Li P., Farkaš I., MacWhinney B.: Early lexical development in a self-organizing neural network. Neural Networks 17(8-9). 1345-1362. 2004.
Farkaš I.: Lexical acquisition and developing semantic map. Neural Network World 13(3). 235-245. 2003.
Farkaš I., Li P.: Modeling the development of lexicon with a growing self-organizing map. In Caulfield, H.J. et al. (eds.), Proceedings of the 6th Joint Conference on Information Sciences, Research Triangle Park, NC. 553-556. 2002.
Li P., Farkaš I.: A self-organizing connectionist model of bilingual processing. In R. Heredia & J. Altarriba (eds.), Bilingual Sentence Processing, North-Holland: Elsevier Science Publisher. 59-85. 2002.
Farkaš I., Li P.: DevLex: A self-organizing neural network model of the development of lexicon. In International Conference on Neural Information Processing (ICONIP), Singapore. 2002.
Farkaš I., Li P.: A self-organizing neural network model of the acquisition of word meaning. In E. Altmann, A. Cleeremans, C. Schunn, & W. Gray (eds.), Proceedings of the 4th International Conference on Cognitive Modeling, Fairfax, VA. 67-72. 2001.

Cognitive semantics and theory of meaning

Related publications:

Vavrečka M., Farkaš I.: A multimodal connectionist architecture for unsupervised grounding of spatial language. Cognitive Computation 6:101-112. 2014.
Wein, A., Takáč, M.: Sensorimotor Characterization of Semantic Structures. Technical report TR-2013-037. Faculty of Mathematics, Physics, and Informatics Comenius University, Bratislava. 2013.
Takáč, M.: Kognitívna sémantika - reprezentácia významov v živých a umelých systémoch. In Sborník studijních materiálů ke kurzu Kognitívní věda a umělá inteligence, Gaudeamus, Hradec Králové. 58-73. 2013.
Takáč, M. and Šefránek, J.: Semantics of distinguishing criteria: from subjective to intersubjective. Interdisciplinary Description of Complex Systems 10(3). 248-269. 2012.

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Rebrová, K., Takáč, M.: Vnímanie a pomenovávanie farieb a farebných kategórií. In Kvasnička, V. et al. (eds.), Umelá inteligencia a kognitívna veda II [Artificial Intelligence and Cognitive Science II], Vydavateľstvo STU, Bratislava. 411 – 436. 2010.
Retová, D., Takáč, M.: Extending the semantics of distinguishing criteria with reasoning. In Kognice. 2010.
Takáč, M.: Construction of Meanings in Biological and Artificial Agents. In Trajkovski, G., Collins, S. G. (eds.), Agent-Based Societies: Social and Cultural Interactions, IGI Global, Hershey, PA. 139-157. 2009.
Takáč, M.: Konštruktivistický prístup k štúdiu kognície. In Kvasnička, V., Pospíchal, J., Kozák, Š., Návrat, P., Paroulek, P. (eds.), Umelá inteligencia a kognitívna veda I [Artificial Intelligence and Cognitive Science I], Vydavateľstvo STU, Bratislava. 395-424. 2009.
Šefránek, J., Takáč, M., Farkaš, I.: Vznik inteligencie v umelých systémoch. In Magdolen, D. (eds.), Hmota, život, inteligencia: Vznik, VEDA, Bratislava. 245-270. 2008.
Takáč, M.: Autonomous Construction of Ecologically and Socially Relevant Semantics. Cognitive Systems Research 9(4). 293-311. . October 2008.
Takáč, M.: Developing Episodic Semantics. In Honkela, T., Pöllä, M., Paukkeri, M., Simula, O. (eds.), Proceedings of AKRR '08, the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, Helsinki University of Technology, Helsinki. 90-96. 2008.
Takáč, M.: Náčrt epizodicky ukotvenej sémantiky. In Kelemen, J., Kvasnička, V., Pstružina, K. (eds.), Kognice a umělý život VIII, Slezská univerzita, Opava. 325-332. 2008.
Takáč, M.: Princípy dizajnu rozumejúcich agentov. In Zborník príspevkov štipendistov z projektu JPD 3 BA 2005/1-043, Knižničné a edičné centrum FMFI UK, Bratislava. 106-109. 2008.
Takáč, M.: Kognitívna sémantika komplexných kategórií založená na rozlišovacích kritériách. In Kvasnička, V., Trebatický, P., Pospíchal, J., Kelemen, J. (eds.), Myseľ, inteligencia a život, Vydavateľstvo STU, Bratislava. 339 - 355. 2007.
Takáč, M.: Construction of Meanings in Living and Artificial Agents (PhD thesis). Comenius University, Bratislava. 2007.
Takáč, M.: Konštrukcia významov a jej dynamika v procese iterovaného učenia. In Kelemen, J., Kvasnička, V., Pospíchal, J. (eds.), Kognice a umělý život VII, Slezská univerzita, Opava. 341-347. 2007.
Šefránek, J., Takáč, M., Farkaš, I.: Vznik inteligencie v umelých systémoch. Technical report TR-2007-001. Comenius University, Bratislava. 2007.
Takáč, M.: When Meanings Are Not Mutually Exclusive: Issues in Receptive Field Based Grounded Cognitive Semantics. Technical report TR-2007-002. Comenius University, Bratislava. 2007.
Takáč, M.: Cognitive Semantics for Dynamic Environments. In Hitzler, P., Schärfe, H., P. Øhrstrøm (eds.), Contribution to ICCS 2006 - 14th International Conference on Conceptual Structures, Aalborg University Press, Aalborg, Denmark. 202-215. 2006.
Takáč, M.: Kognitívna sémantika rozlišovacích kritérií. In Kelemen, J., Kvasnička, V. (eds.), Kognice a umělý život VI, Slezská univerzita, Opava. 363-372. 2006.

Adaptive knowledge representation, concept learning (multimodal representations)

Related publications:

Reinboth T., Farkaš I.: Ultimate grounding of abstract concepts: A graded account. Journal of Cognition, 5(1). 2022.
Farkaš I.: On abstraction: psychological, neural, and computational perspectives. In Kognice a umělý život (KUZ). 2016.
Gergeľ P., Farkaš I.: Connectionist modeling of part–whole analogy learning. In Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science. 587-592. 2015.
Farkaš I.: Paradigmy a metódy výskumu v kognitívnej vede. In Sborník studijních materiálů ke kurzu Kognitívní věda a umělá inteligence, Gaudeamus, Hradec Králové. 6-21. 2013.

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Vavrečka M., Farkaš I.: Unsupervised grounding of spatial relations. In Kokinov B., Karmiloff-Smith A., and Nersessian N. (eds.), Proceedings of the European Conference on Cognitive Science, Sofia, New Bulgarian University Press, Sofia. 2011.
Vavrečka M., Farkaš I., Lhotská L.: Bio-inspired model of spatial cognition. In International Conference on Neural Information Processing (ICONIP). 2011.
Retová, D., Takáč, M.: Extending the semantics of distinguishing criteria with reasoning. In Kognice. 2010.
Farkaš I.: Významy ako mentálne simulácie v situačnom modeli. In Kelemen J., Kvasnička V., Rybár J. (eds.), Kognice a umelý život IX, Slezská univerzita, Opava. 69-76. 2009.
Takáč, M.: Construction of Meanings in Biological and Artificial Agents. In Trajkovski, G., Collins, S. G. (eds.), Agent-Based Societies: Social and Cultural Interactions, IGI Global, Hershey, PA. 139-157. 2009.
Takáč, M.: Konštruktivistický prístup k štúdiu kognície. In Kvasnička, V., Pospíchal, J., Kozák, Š., Návrat, P., Paroulek, P. (eds.), Umelá inteligencia a kognitívna veda I [Artificial Intelligence and Cognitive Science I], Vydavateľstvo STU, Bratislava. 395-424. 2009.
Takáč, M.: Autonomous Construction of Ecologically and Socially Relevant Semantics. Cognitive Systems Research 9(4). 293-311. . October 2008.
Takáč, M.: Developing Episodic Semantics. In Honkela, T., Pöllä, M., Paukkeri, M., Simula, O. (eds.), Proceedings of AKRR '08, the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, Helsinki University of Technology, Helsinki. 90-96. 2008.
Takáč, M.: Náčrt epizodicky ukotvenej sémantiky. In Kelemen, J., Kvasnička, V., Pstružina, K. (eds.), Kognice a umělý život VIII, Slezská univerzita, Opava. 325-332. 2008.
Takáč, M.: Kognitívna sémantika komplexných kategórií založená na rozlišovacích kritériách. In Kvasnička, V., Trebatický, P., Pospíchal, J., Kelemen, J. (eds.), Myseľ, inteligencia a život, Vydavateľstvo STU, Bratislava. 339 - 355. 2007.
Takáč, M.: Construction of Meanings in Living and Artificial Agents (PhD thesis). Comenius University, Bratislava. 2007.
Takáč, M.: Konštrukcia významov a jej dynamika v procese iterovaného učenia. In Kelemen, J., Kvasnička, V., Pospíchal, J. (eds.), Kognice a umělý život VII, Slezská univerzita, Opava. 341-347. 2007.
Takáč, M.: When Meanings Are Not Mutually Exclusive: Issues in Receptive Field Based Grounded Cognitive Semantics. Technical report TR-2007-002. Comenius University, Bratislava. 2007.
Takáč, M.: Cognitive Semantics for Dynamic Environments. In Hitzler, P., Schärfe, H., P. Øhrstrøm (eds.), Contribution to ICCS 2006 - 14th International Conference on Conceptual Structures, Aalborg University Press, Aalborg, Denmark. 202-215. 2006.
Takáč, M.: Kognitívna sémantika rozlišovacích kritérií. In Kelemen, J., Kvasnička, V. (eds.), Kognice a umělý život VI, Slezská univerzita, Opava. 363-372. 2006.
Takáč, M.: Categorization by Sensory-Motor Interaction in Artificial Agents. In Fum, D., Del Missier, F., Stocco, A. (eds.), Proceedings of the 7th International Conference on Cognitive Modeling, Edizioni Goliardiche, Trieste, Italy. 310-315. 2006.
Takáč, M.: Návrh kognitívnej architektúry pre jazykové experimenty. In Kelemen, J., Kvasnička,V., Pospíchal, J. (eds.), Kognice a umělý život V., Slezská univerzita, Opava. 549-562. 2005.

Higher cognition, motivational system, planning, reasoning

Related publications:

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].
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.
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.
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.

Language evolution, origins of language

Related publications:

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.
Takáč, M.: Modelovanie kultúrneho prenosu a jeho úloha v evolúcii jazyka. In Rybár, J., Kvasnička, V., Farkaš, I. (eds.), Jazyk a kognícia, Kalligram, Bratislava. 323-360. 2005.
Takáč, M.: Návrh kognitívnej architektúry pre jazykové experimenty. In Kelemen, J., Kvasnička,V., Pospíchal, J. (eds.), Kognice a umělý život V., Slezská univerzita, Opava. 549-562. 2005.
Takáč, M.: Koevolučné modelovanie vzniku jazyka. In Kelemen, J. (eds.), Kognice a umělý život III, Slezská univerzita, Opava. 197-205. 2003.

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Bodík, P., Takáč, M.: Formation of a Common Spatial Lexicon and its Change in a Community of Moving Agents. In Tessem, B., Ala-Siuru, P., Doherty, P., Mayoh, B. (eds.), Frontiers in AI: Proceedings of the Eighth Scandinavian Conference on Artificial Intelligence (SCAI'03), IOS Press, Amsterdam. 37-46. 2003.
Takáč, M.: Kultúrna dynamika v koevolúcii jazyka. In Sinčák, P., Kvasnička, V., Pospíchal, J., Kelemen, J., Návrat, P. (eds.), Slovensko-České rozpravy o umelej inteligencii: Proceedings of CALCI-03, Elfa, Košice. 263-268. 2003.
Takáč, M.: Emergencia lingvistických fenoménov v jazykových hrách. 2001. Dištančný kurz Kognitívne vedy 2000/2001, STU Bratislava.

Qualitative modeling

Related publications:

Takáč, M.: Kvalitatívne modelovanie a simulácia. Comenius University Press, Bratislava. 2003.
Takáč, M.: Fixed Point Classification Method for Qualitative Simulation. Comenius University, Bratislava. 2001. Unpublished RNDr. thesis.
Takáč, M.: A Qualitative System Theory Based Method for Stability Checking and Its Applicability to Qualitative Reasoning. Comenius University, Bratislava. 1997. Unpublished master thesis.
Takáč, M.: Fixed Point Classification Method for Qualitative Simulation. In Costa, E., Cardoso, A. (eds.), Progress in Artificial Intelligence: Proceedings of the Eighth Portuguese Conference on Artificial Intelligence (EPIA '97), Springer, Berlin. 255-266. 1997.