Angleraud, A., Houbre, Q., Kyrki, V., & Pieters, R. (2018). Human-robot interactive learning architecture using ontologies and symbol manipulation. teoksessa RO-MAN 2018 - 27th IEEE International Symposium on Robot and Human Interactive Communication: August 27-31, 2018, Nanjing, China. (Sivut 384-389). (IEEE RO-MAN). IEEE. https://doi.org/10.1109/ROMAN.2018.8525580

Angleraud, A., Houbre, Q., & Pieters, R. (2019). Teaching semantics and skills for human-robot collaboration. Paladyn, 10(1), 318-329. https://doi.org/10.1515/pjbr-2019-0025

Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., ... Klein, S. (2015). Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, 111, 562-579. https://doi.org/10.1016/j.neuroimage.2015.01.048

Chen, K., & Zhang, Z. (2018). A Primal Neural Network for Online Equality-Constrained Quadratic Programming. Cognitive Computation, 10(2), 381–388. https://doi.org/10.1007/s12559-017-9510-4

Faisal, A., Gillberg, J., Leen, G., & Peltonen, J. (2013). Transfer learning using a nonparametric sparse topic model. Neurocomputing, 112, 124-137. https://doi.org/10.1016/j.neucom.2012.12.038

Gavas, R. D., Tripathy, S. R., Chatterjee, D., & Sinha, A. (2018). Cognitive load and metacognitive confidence extraction from pupillary response. Cognitive Systems Research, 52, 325-334. https://doi.org/10.1016/j.cogsys.2018.07.021

Iantovics, L. B., Emmert-Streib, F., & Arik, S. (2017). MetrIntMeas a novel metric for measuring the intelligence of a swarm of cooperating agents. Cognitive Systems Research, 45, 17-29. https://doi.org/10.1016/j.cogsys.2017.04.006

Iosifidis, A., Tefas, A., & Pitas, I. (2015). Distance-based human action recognition using optimized class representations. Neurocomputing, 161, 47-55. https://doi.org/10.1016/j.neucom.2014.10.088

Iosifidis, A., Tefas, A., & Pitas, I. (2015). DropELM: Fast neural network regularization with Dropout and DropConnect. Neurocomputing, 162, 57-66. https://doi.org/10.1016/j.neucom.2015.04.006

Iosifidis, A., Tefas, A., & Pitas, I. (2014). Regularized extreme learning machine for multi-view semi-supervised action recognition. Neurocomputing, 145, 250-262. https://doi.org/10.1016/j.neucom.2014.05.036

Iosifidis, A. (2015). Extreme learning machine based supervised subspace learning. Neurocomputing, 167, 158–164. https://doi.org/10.1016/j.neucom.2015.04.083

Iosifidis, A., Tefas, A., & Pitas, I. (2013). Learning sparse representations for view-independent human action recognition based on fuzzy distances. Neurocomputing, 121, 344-353. https://doi.org/10.1016/j.neucom.2013.05.021

Lolicato, F., Juhola, H., Zak, A., Postila, P. A., Saukko, A., Rissanen, S., ... Róg, T. (2020). Membrane-Dependent Binding and Entry Mechanism of Dopamine into Its Receptor. ACS Chemical Neuroscience, 11(13), 1914–1924. https://doi.org/10.1021/acschemneuro.9b00656

Miinalainen, T., Rezaei, A., Us, D., Nüßing, A., Engwer, C., Wolters, C. H., & Pursiainen, S. (2019). A realistic, accurate and fast source modeling approach for the EEG forward problem. NeuroImage, 184(1), 56-67. https://doi.org/10.1016/j.neuroimage.2018.08.054

Mokkila, S., Postila, P. A., Rissanen, S., Juhola, H., Vattulainen, I., & Róg, T. (2017). Calcium Assists Dopamine Release by Preventing Aggregation on the Inner Leaflet of Presynaptic Vesicles. ACS Chemical Neuroscience, 8(6), 1242-1250. https://doi.org/10.1021/acschemneuro.6b00395

Moradi, E., Khundrakpam, B., Lewis, J. D., Evans, A. C., & Tohka, J. (2017). Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data. NeuroImage, 144(A), 128–141. https://doi.org/10.1016/j.neuroimage.2016.09.049

Möttönen, T., Katisko, J., Haapasalo, J., Tähtinen, T., Kiekara, T., Kähärä, V., ... Lehtimäki, K. (2015). Defining the anterior nucleus of the thalamus (ANT) as a deep brain stimulation target in refractory epilepsy: Delineation using 3 T MRI and intraoperative microelectrode recording. NeuroImage: Clinical, 7, 823-829. https://doi.org/10.1016/j.nicl.2015.03.001

Pajarinen, J., Peltonen, J., & Uusitalo, M. A. (2011). Fault tolerant machine learning for nanoscale cognitive radio. Neurocomputing, 74(5), 753-764. https://doi.org/10.1016/j.neucom.2010.10.007

Rimpiläinen, V., Koulouri, A., Lucka, F., Kaipio, J. P., & Wolters, C. H. (2019). Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. NeuroImage, 188, 252-260. https://doi.org/10.1016/j.neuroimage.2018.11.058

Sciacca, M. F. M., Romanucci, V., Zarrelli, A., Monaco, I., Lolicato, F., Spinella, N., ... Milardi, D. (2017). Inhibition of Aβ Amyloid Growth and Toxicity by Silybins: The Crucial Role of Stereochemistry. ACS Chemical Neuroscience, 8(8), 1767-1778. https://doi.org/10.1021/acschemneuro.7b00110

Sun, L., Peräkylä, J., Polvivaara, M., Öhman, J., Peltola, J., Lehtimäki, K., ... Hartikainen, K. M. (2015). Human anterior thalamic nuclei are involved in emotion-attention interaction. NEUROPSYCHOLOGIA, 78, 88-94. https://doi.org/10.1016/j.neuropsychologia.2015.10.001

Tran, D. T., Iosifidis, A., & Gabbouj, M. (2018). Improving efficiency in convolutional neural networks with multilinear filters. Neural Networks, 105, 328-339. https://doi.org/10.1016/j.neunet.2018.05.017

Waris, M. A., Iosifidis, A., & Gabbouj, M. (2017). CNN-based edge filtering for object proposals. Neurocomputing, 266, 631-640. https://doi.org/10.1016/j.neucom.2017.05.071

Wortha, S. M., Bloechle, J., Ninaus, M., Kiili, K., Lindstedt, A., Bahnmueller, J., ... Klein, E. (2020). Neurofunctional plasticity in fraction learning: An fMRI training study. Trends in Neuroscience and Education, 21, [100141]. https://doi.org/10.1016/j.tine.2020.100141

Xiao, L., Liao, B., Li, S., & Chen, K. (2018). Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations. Neural Networks, 98, 102-113. https://doi.org/10.1016/j.neunet.2017.11.011