Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network. - Roman Rosipal; Milos Koska; Igor Farkas (Neural Process. Lett., 1998)

Modeling the Development of Lexicon with a Growing Self-Organizing Map. - Igor Farkas; Ping Li (JCIS, 2002)

Early lexical development in a self-organizing neural network. - Ping Li 026; Igor Farkas; Brian MacWhinney (Neural Networks, 2004)

On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. - Peter Tiño; Igor Farkas (ICNC (2), 2005)

Recursive Self-organizing Map as a Contractive Iterative Function System. - Peter Tiño; Igor Farkas; Jort van Mourik (IDEAL, 2005)

Dynamics and Topographic Organization of Recursive Self-Organizing Maps. - Peter Tiño; Igor Farkas; Jort van Mourik (Neural Comput., 2006)

Systematicity in sentence processing with a recursive self-organizing neural network. - Igor Farkas; Matthew W. Crocker (ESANN, 2007)

Learning Nonadjacent Dependencies with a Recurrent Neural Network. - Igor Farkas (ICONIP (2), 2008)

Syntactic systematicity in sentence processing with a recurrent self-organizing network. - Igor Farkas; Matthew W. Crocker (Neurocomputing, 2008)

Recursive Self-organizing Networks for Processing Tree Structures - Empirical Comparison. - Pavol Vanco; Igor Farkas (IJCCI, 2009)

Segmentation and supervised classification of image objects in Epo doping-control. - Ivan Bajla; Frantisek Rublík; Barbora Arendacká; Igor Farkas; Klára Hornisová; Svorad Stolc; Viktor Witkovský (Mach. Vis. Appl., 2009)

Experimental comparison of recursive self-organizing maps for processing tree-structured data. - Pavol Vanco; Igor Farkas (Neurocomputing, 2010)

Modeling Utterance-mediated Attention in Situated Language Comprehension. - Jan Svantner; Igor Farkas; Matthew W. Crocker (CogSci, 2011)

Bio-inspired Model of Spatial Cognition. - Michal Vavrecka; Igor Farkas; Lenka Lhotská (ICONIP (1), 2011)

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning. - Igor Farkas; Tomás Malík; Kristína Rebrová (Frontiers Neurorobotics, 2012)

Bidirectional Activation-based Neural Network Learning Algorithm. - Igor Farkas; Kristína Rebrová (ICANN, 2013)

Towards a robotic model of the mirror neuron system. - Kristína Rebrová; Matej Pechác; Igor Farkas (ICDL-EPIROB, 2013)

Calculation of object position in various reference frames with a robotic simulator. - Marcel Svec; Igor Farkas (CogSci, 2014)

Memory Capacity of Input-Driven Echo State Networks at the Edge of Chaos. - Peter Barancok; Igor Farkas (ICANN, 2014)

A Multimodal Connectionist Architecture for Unsupervised Grounding of Spatial Language. - Michal Vavrecka; Igor Farkas (Cogn. Comput., 2014)

Connectionist Modeling of Part-Whole Analogy Learning. - Peter Gergel; Igor Farkas (EAPCogSci, 2015)

Computational analysis of the Bidirectional Activation-based Learning in autoencoder task. - Peter Csiba; Igor Farkas (IJCNN, 2015)

Computational analysis of memory capacity in echo state networks. - Igor Farkas; Radomír Bosák; Peter Gergel (Neural Networks, 2016)

Maximizing memory capacity of echo state networks with orthogonalized reservoirs. - Igor Farkas; Peter Gergel (IJCNN, 2017)

Assessment and training of visuospatial cognitive functions in virtual reality: proposal and perspective. - Stefan Korecko; Marián Hudák; Branislav Sobota; Martin Marko; Barbora Cimrová; Igor Farkas; Roman Rosipal (CogInfoCom, 2018)

Investigating the Role of Astrocyte Units in a Feedforward Neural Network. - Peter Gergel; Igor Farkas (ICANN (3), 2018)

Towards More Biologically Plausible Error-Driven Learning for Artificial Neural Networks. - Kristína Malinovská; Ludovít Malinovský; Igor Farkas (ICANN (3), 2018)

Computational Analysis of Learned Representations in Deep Neural Network Classifiers. - Tomas Kuzma; Igor Farkas (IJCNN, 2018)

Evaluation of Information-Theoretic Measures in Echo State Networks on the Edge of Stability. - Miloslav Torda; Igor Farkas (IJCNN, 2018)

Computational model of enactive visuospatial mental imagery using saccadic perceptual actions. - Jan Jug; Tine Kolenik; André Ofner; Igor Farkas (Cogn. Syst. Res., 2018)

Robotic Homunculus: Learning of Artificial Skin Representation in a Humanoid Robot Motivated by Primary Somatosensory Cortex. - Matej Hoffmann; Zdenek Straka; Igor Farkas; Michal Vavrecka; Giorgio Metta (IEEE Trans. Cogn. Dev. Syst., 2018)

Experimental Procedure for Evaluation of Visuospatial Cognitive Functions Training in Virtual Reality. - Stefan Korecko; Branislav Sobota; Marián Hudák; Igor Farkas; Barbora Cimrová; Peter Vasil; Dominik Trojcák (AISI, 2019)

UBAL: a Universal Bidirectional Activation-based Learning Rule for Neural Networks. - Kristína Malinovská; Ludovít Malinovský; Pavel Krsek; Svatopluk Kraus; Igor Farkas (CIIS, 2019)

Embedding Complexity of Learned Representations in Neural Networks. - Tomas Kuzma; Igor Farkas (ICANN (2), 2019)

Modeling Self-organized Emergence of Perspective In/variant Mirror Neurons in a Robotic System. - Jakub Pospíchal; Igor Farkas; Matej Pechác; Kristína Malinovská (ICDL-EPIROB, 2019)

Echo State Networks with Artificial Astrocytes and Hebbian Connections. - Peter Gergel; Igor Farkas (IWANN (1), 2019)

Computational Analysis of Robustness in Neural Network Classifiers. - Iveta Becková; Stefan Pócos; Igor Farkas (ICANN (1), 2020)

Adaptive Skill Acquisition in Hierarchical Reinforcement Learning. - Juraj Holas; Igor Farkas (ICANN (2), 2020)

Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers. - Stefan Pócos; Iveta Becková; Tomás Kuzma; Igor Farkas (TAILOR, 2020)

Advances in Adaptive Skill Acquisition. - Juraj Holas; Igor Farkas (ICANN (4), 2021)

Generative Properties of Universal Bidirectional Activation-Based Learning. - Kristína Malinovská; Igor Farkas (ICANN (3), 2021)

Intrinsic Motivation Model Based on Reward Gating. - Matej Pechác; Igor Farkas (ICANN (4), 2021)

Semi-supervised Learning in Camera Surveillance Image Classification. - Matús Tuna; Kristína Malinovská; Igor Farkas; Svatopluk Kraus; Pavel Krsek (ICCP, 2021)

Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks. - Stefan Pócos; Iveta Becková; Igor Farkas (ICANN (4), 2022)

A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot. - Kristína Malinovská; Igor Farkas; Jana Harvanová; Matej Hoffmann (ICDL, 2022)

Safe Reinforcement Learning in a Simulated Robotic Arm. - Luka Kovac; Igor Farkas (ICANN (1), 2023)

Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models. - Andrej Lúcny; Kristína Malinovská; Igor Farkas (ICANN (1), 2023)

Appearance-Based Gaze Estimation Enhanced with Synthetic Images Using Deep Neural Networks. - Dmytro Herashchenko; Igor Farkas (SSCI, 2023)

Exploration by self-supervised exploitation. - Matej Pechác; Michal Chovanec; Igor Farkas (CoRR, 2023)

Learning Low-Level Causal Relations Using a Simulated Robotic Arm. - Miroslav Cibula; Matthias Kerzel; Igor Farkas (ICANN (10), 2024)

RecViT: Enhancing Vision Transformer with Top-Down Information Flow. - Stefan Pócos; Iveta Becková; Igor Farkas (VISIGRAPP (3): VISAPP, 2024)

Self-supervised network distillation: An effective approach to exploration in sparse reward environments. - Matej Pechác; Michal Chovanec; Igor Farkas (Neurocomputing, 2024)

Generating and Customizing Robotic Arm Trajectories using Neural Networks. - Andrej Lúcny; Matilde Antonj; Carlo Mazzola; Hana Hornácková; Igor Farkas (CoRR, 2025)

Robotic Calibration Based on Haptic Feedback Improves Sim-to-Real Transfer. - Juraj Gavura; Michal Vavrecka; Igor Farkas; Connor Gade (CoRR, 2025)

Examining the legibility of humanoid robot arm movements in a pointing task. - Andrej Lúcny; Matilde Antonj; Carlo Mazzola; Hana Hornácková; Ana Faric; Kristína Malinovská; Michal Vavrecka; Igor Farkas (CoRR, 2025)

Toward an Interaction-Centered Approach to Robot Trustworthiness. - Carlo Mazzola; Hassan Ali; Kristína Malinovská; Igor Farkas (CoRR, 2025)