Publications - Thematic Area A

Badde, S., Röder, B., & Bruns, P. (in press). Task-irrelevant sounds influence both temporal order and apparent motion judgments about tactile stimuli applied to crossed and uncrossed hands. Attention, Perception, & Psychophysics, in press.

Bruns, P., Röder, B., (in press). Spatial and frequency specificity of the ventriloquism aftereffect revisited. Psychological Research. doi: 10.1007/s00426-017-0965-4

Katzakis, N., Tong, J., Nunez, O., Chen, L., Klinker, G., Röder, B., & Steinicke, F. (2017). Stylo and handifact: modulating haptic perception through visualisations for posture training in augmented reality. Proc. Spatial User Interaction, doi: 10.1145/3131277.3132181

Zierul, B., Röder, B., Tempelmann, C., Bruns, P., and Noesselt, T. (2017) The Role of Auditory Cortex in the Spatial Ventriloquism Aftereffect. NeuroImage, 162, 257-268.

Zhang, D., Hong, B., Gao, S., & Röder, B. (2017). Exploring the temporal dynamics of sustained and transient spatial attention using steady-state visual evoked potentials. Experimental Brain Research, 2017 May; 235(5):1575-1591. doi: 10.1007/s00221-017-4907-6.

Bruns, P., Röder, B. (in press). Cross-modal learning in the auditory system. In A. K. C. Lee, M. Wallace, A. Coffin, A. N. Popper, & R. R. Fay (Eds.), Multisensory processes: The auditory perspective. Springer Handbook of Auditory Research.

Zierul, B., Tong, J., Bruns, P., & Röder, B. 2018. (in press). Reduced multisensory integration of self-initiated stimuli, Cognition.

Badde, S., Röder, B., Heed, T. (2014). Multiple spatial representations determine touch localization. Journal of Experimental Psychology: Human Perception and Performance, 40 (2), 784-801.

Bruns, P., Maiworm, M., & Röder, B. (2014) Reward expectation influences audiovisual spatial integration. Attention, Perception, & Psychophysics, 76, 1815-1827.

Bruns, P., Liebnau, R., & Röder, B. (2011). Crossmodal training induces changes in spatial representations early in the auditory processing pathway. Psychological Science, 22(9), 1120-1126.

Bruns, P. & Röder, B. (2015). Sensory recalibration integrates information from the immediate and the cumulative past. Scientific Reports, in press.

Bruns, P., Spence, C., & Röder, B. (2011). Tactile recalibration of auditory spatial representations. Experimental Brain Research, 209 (3), 333-344.

Bruns, P., & Röder, B. (2010). Tactile capture of auditory localization: An event-related potential study. European Journal of Neuroscience, 31, 1844-1857.

Heed, T., Buchholz, V., Engel, A., & Röder, B. (2015). Tactile remapping: from coordinate transformation to integration in sensorimotor processing. Trends in Cognitive Sciences, 19(5), 251-258.

Heed, T., Röder, B. (2014). Motor coordination uses external spatial coordinates independent of developmental vision. Cognition, 132, 1-15.

Maiworm, M., Bellantoni, M., Spence, C., & Röder, B. (2012). When emotional valence modulates audiovisual integration. Attention, Perception & Psychophysics, 74 (6), 1302-1311.

Zhang, D., Hong, B., Gao, X., Gao, S., Röder, B. (2011). Exploring steady-state visual evoked potentials as an index for intermodal and crossmodal spatial attention. Psychophysiology, 48, 665-675.

 

Zhu T, Liang C, Li D, Tian M, Liu S, Gao G and Guan JS*. (2016) Histone methyltransferase Ash1L mediates activity-dependent repression of neurexin-1α Sci Rep. 6: 26597.

Ruxing Fu, Wenhan Luo, Roy Nazempour, Daxin Tan, He Ding, Kaiyuan Zhang, Lan Yin, Ji-Song Guan*, Xin Sheng*. (2018). Implantable and Biodegradable Poly(L-lactic acid) Fibers for Optical Neural Interfaces, (2018), Adv Optical Materials. DOI: 10.1002/adom.201700941

Jiang.J. Guanyu Wang, Ji-Song Guan. (2018). Mammillary body regulates state-dependent fear by alternating cortical oscillations Sci Rep (in press)

Fretter C, Lesne A, Hilgetag CC, Hütt MT. (2017). Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs. Sci Rep 7:42340.

Toba M, Zavaglia M, Rastelli F, Valabrégue R, Pradat-Diehl P, Valero-Cabré A, Hilgetag CC. (2017). Game theoretical mapping of causal interactions underlying visuo-spatial attention in the human brain based on stroke lesions. Hum Brain Mapp. doi: 10.1002/hbm.23601.

Damicelli F, Hilgetag CC, Hütt MT, Messé A. (2017). Modular topology emerges from plasticity in a minimalistic excitable network model. Chaos. 27(4):047406.

Chen Y, Wang S, Hilgetag CC, Zhou C. (2017). Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency. PLoS Comput Biol 13(9):e1005776.

Maier-Hein KH, Neher PF, ..., Hilgetag CC, Stieltjes B, Descoteaux M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8(1):1349.

Goulas A, Zilles K, Hilgetag CC. (2018). Cortical gradients and laminar projections in mammals. Trends Neurosci pii: S0166-2236(18)30164-4

Goulas A, Uylings H, Hilgetag C (2016) Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse BRAIN STRUCT FUNCT. 222(3):1281-1295

Messé A, Hütt MT, Hilgetag CC (2018). Toward a theory of coactivation patterns in excitable neural networks. PLoS Comput Biol 14(4):e1006084.

Damicelli F, Hilgetag CC, Hutt MT, Messé A. (2018). Topological reinforcement as a principle of modularity emergence in brain networks. bioRxiv http://dx.doi.org/10.1101/408278

Ding X, Liu S, Tian M, Zhang W, Zhu T, Li D, Wu J, Deng H, Jia Y, Xie W, Xie H, Guan JS (2017). Activity-induced histone modifications govern Neurexin-1 mRNA splicing and memory preservation. Nat Neurosci. 2017 May; 20(5):690-699.

Ji Song Guan, Jun Jiang, Hong Xie, Kai-Yuan Liu (2016). How does the sparse memory ``engram'' neurons encode the memory of a spatial-temporal event? Frontiers in Neural Circuits. 2016 Aug (in print)

Guan, J.S., Xie, H., Ding, X. (2015). The role of epigenetic regulation in learning and memory. Exp Neurol. 2015 Jun; 268:30-6.

Xu K, Su H, Zhu J, Guan JS and Zhang B. (2016). Neuron segmentation based on CNN with semi-supervised regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20-28

Zavaglia M, Forkert N, Cheng B, Gerloff C, Thomalla G, Hilgetag C (2016). Technical considerations of a game-theoretical approach for lesion symptom mapping, BMC Neurosci 17:40

Malherbe C, Umarova R, Zavaglia M, Kaller C, Beume L, Thomalla G, Weiller C, Hilgetag C (2017). Neural correlates of visuospatial bias in patients with left hemisphere stroke: a causal functional contribution analysis based on game theory, Neuropsychologia, 115: 142-153.

Hilgetag C, Amunts K (2016) Connectivity and cortical architecture e-Neuroforum. 7(3):56-63.

Hilgetag C, Medalla M, Beul S, Barbas H (2016) The primate connectome in context: Principles of connections of the cortical visual system. Neuroimage. 134:685-702.

Ji-Song Guan，Hong Xie, San-Xiong Liu (2017) Epigenetic regulators sculpt the plastic brain. Frontiers in Biology 12(5): 317–332

Stellmann J, Hodecker S, Cheng B, Wanke N, Young K, Hilgetag C, Gerloff C, Heesen C, Thomalla G, Siemonsen S (2017) Reduced rich-club connectivity is related to disability in primary progressive MS Neurol Neuroimmunol Neuroinflamm. 4(5):e375

Rollenhagen A, Ohana O, Sätzler K, Hilgetag C, Kuhl D, Lübke J (2018) Structural Properties of Synaptic Transmission and Temporal Dynamics at Excitatory Layer 5B Synapses in the Adult Rat Somatosensory Cortex Front Synaptic Neurosci. 10:24.

Zou Y, Zhao Z, Yin D, Fan M, Small M, Liu Z, Hilgetag C, Kurths (2018) Brain anomaly networks uncover heterogeneous functional reorganization patterns after stroke NEUROIMAGE-CLIN. 20:523-530

Gräff J, Rei D., Guan J. S., Wang W. Y., Seo J., Hennig K. M., Nieland T. J., Fass D. M., Kao P. F., Kahn M., Su S. C., Samiei A., Joseph N., Haggarty S. J., Delalle I., & Tsai L. H. (2012). An epigenetic blockade of cognitive functions in the neurodegenerating brain, Nature, 483:222.
Guan J. S., Haggarty S. J., Giacometti E., Dannenberg J. H., Joseph N., Gao J., Nieland T. J., Zhou Y., Wang X., Mazitschek R., Bradner J. E., DePinho R. A., Jaenisch R., & Tsai L. H. (2009). HDAC2 negatively regulates memory formation and synaptic plasticity, Nature, 459: 55.
Hilgetag C. C., O'Neill M. A., & Young M. P. (1996). Indeterminate organization of the visual system, Science, 271: 776.
Hilgetag C. C., Théoret H., & Pascual-Leone A. (2001). Enhanced visual spatial attention ipsilateral to rTMS-induced ‘virtual lesions’ of human parietal cortex, Nature Neuroscience, 4, 953.
Keinan A., Sandbank B., & Hilgetag C. C. (2004). Meilijson I, Ruppin E: Fair attribution of functional contribution in artificial and biological networks, Neural Computation, 16: 1887.
Keinan A., Sandbank B., & Hilgetag C. C., Meilijson I, Ruppin E (2006). Axiomatic scalable neurocontroller analysis via the Shapley value, Artificial Life, 12: 333.
Sporns O., Chialvo D., Kaiser M., & Hilgetag C. C. (2004). Organization, development and function of complex brain networks, Trends in Cognitive Sciences, 8: 418-425.
Xie H., Liu Y., & Guan J. S. (2014). In vivo imaging of immediate early gene expression reveals layer-specific memory traces in the mammalian brain, PNAS, 111(7): 2788.
Young M. P., Hilgetag C. C., & Scannell J. W. (2000). On imputing function to structure from the behavioural effects of brain lesions, Phil Trans R Soc Lond B, 355: 147.
Zavaglia M., & Hilgetag C. C. (2015). Causal functional contributions and interactions in the attention network of the brain: An objective multi-perturbation analysis, Brain Struct Funct, DOI: 10.1007/s00429-015-1058-z.

 

Focko Higgen, Philipp Ruppel, Michael Görner, Matthias Kerzel, Sven Magg, Norman Hendrich, (2018). Crossmodal Pattern Discrimination in Humans and Robots: A Visuo-Tactile Case Study, Workshop on Crossmodal Learning, IROS-2018, Madrid

Cohen, L. G., Celnik, P., Pascual-Leone, A., Corwell, B., Falz, L., Dambrosia, J., Honda, M., Sadato, N., Gerloff, C., Catalá, M.D., & Hallett, M. (1997). Functional relevance of crossmodal plasticity in blind humans. Nature, 389(6647), 180-183.
Heise, K. F., Zimerman, M., Hoppe, J., Gerloff, C., Wegscheider, K., & Hummel, F. C. (2013). The aging motor system as a model for plastic changes of GABA-mediated intracortical inhibition and their behavioral relevance. J Neurosci, 33(21), 9039-9049.
Hummel, F., & Gerloff, C. (2005). Larger interregional synchrony is associated with greater behavioral success in a complex sensory integration task in humans. Cereb Cortex, 15(5), 670-678.
Hummel, F. C., Heise, K., Celnik, P., Floel, A., Gerloff, C., & Cohen, L. G. (2010). Facilitating skilled right hand motor function in older subjects by anodal polarization over the left primary motor cortex. Neurobiol Aging, 31(12), 2160-2168.
Plewnia, C., Rilk, A. J., Soekadar, S. R., Arfeller, C., Huber, H. S., Sauseng, P., Hummel, F.C., & Gerloff, C. (2008). Enhancement of long-range EEG coherence by synchronous bifocal transcranial magnetic stimulation. Eur J Neurosci, 27(6), 1577-1583.
Renzi, C., Bruns, P., Heise, K. F., Zimerman, M., Feldheim, J. F., Hummel, F. C., & Roder, B. (2013). Spatial remapping in the audio-tactile ventriloquism effect: a TMS investigation on the role of the ventral intraparietal area. J Cogn Neurosci, 25(5), 790-801.
Sauseng, P., Klimesch, W., Heise, K. F., Gruber, W. R., Holz, E., Karim, A. A., Glennon, M., Gerloff, C., Birbaumer, N., & Hummel, F. C. (2009). Brain oscillatory substrates of visual short-term memory capacity. Curr Biol, 19(21), 1846-1852.

Schulz, R., Zimerman, M., Timmermann, J. E., Wessel, M. J., Gerloff, C., & Hummel, F. C. (2014). White matter integrity of motor connections related to training gains in healthy aging. Neurobiol Aging, 35(6), 1404-1411.
Xue, G., Dong, Q., Chen, C., Lu, Z., Mumford, J. A., & Poldrack, R. A. (2010). Greater neural pattern similarity across repetitions is associated with better memory. Science, 330(6000), 97-101.
Zimerman, M., Nitsch, M., Giraux, P., Gerloff, C., Cohen, L. G., & Hummel, F. C. (2013). Neuroenhancement of the aging brain: restoring skill acquisition in old subjects. Ann Neurol, 73(1), 10-15.

      

Lu R., Duan Z. and Zhang C. (2018). Listen and Look: Audio–Visual Matching Assisted Speech Source Separation. IEEE Signal Processing Letters.

Guan H., Zhang J. (2017). Multi-sensory based novel household object categorization system by using interactive behaviours. IEEE International Conference on Robotics and Biomimetics.

Zhang, L., He, B., Zhang, J., (2016). Multimodal Information Representation Based on Approximation Theory (in Chinese). Science Press, ISBN: 978-7-03-050253-7.

Liu, L., He, B., Zhuang, J., Zhang, L., Lv, A., (2017). Force Measurement System for Invisalign Based on Thin Film Single Force Sensor. Measurement 97C, pp. 1-7. http://www.sciencedirect.com/science/article/pii/S0263224116306558

Kun Fu, Junqi Jin, Runpeng Cui, Fei Sha, Changshui Zhang (2017). Aligning where to see and what to tell: image captioning with region-based attention and scene-specific contexts. IEEE transactions on pattern analysis and machine intelligence 39.12 (2017): 2321-2334.

Dejun Chu, Changshui Zhang, and Qing Tao. (2017). A faster cutting plane algorithm with accelerated line search for linear SVM. Pattern Recognition. Volume 67, Pages 127-138, July 2017

Wenzheng Hu, Qing Zhuo, Jianke Li, Changshui Zhang. (2017). Fast Branch Convolutional Neural Network for Traffic Sign Recognition. IEEE Intelligent Transportation Systems Magazine 9.3 (2017): 114-126.

Jiang Lu, Jie Hu, Guannan, Zhao, Fenghua Mei and Changshui Zhang. (2017). An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture 142 (2017): 369-379.

Daqing Chang, Ming Lin, and Changshui Zhang. (2018). On the Generalization Ability of Online Gradient Descent Algorithm Under the Quadratic Growth Condition. IEEE Transactions on Neural Networks and Learning Systems (2018).

Kun, Fu; Jin, Li; Junqi, Jin; Changshui, Zhang. (2018). Image-Text Surgery: Efficient Concept Learning in Image Captioning by Generating Pseudo Pairs. IEEE Transactions on Neural Networks and Learning Systems (2018).

Chengzhe Yan, Jie Hu, and Changshui Zhang. (2018). Deep transformer: A framework for 2D text image rectification from planar transformations. Neurocomputing 289 : 32-43.

Jiang Lu, Jin Li, Ziang Yan, Fenghua Mei and Changshui Zhang. (2018). Attribute-Based Synthetic Network (ABS-Net): Learning more from pseudo feature representations. Pattern Recognition 80 : 129-142.

Dejun Chu, Rui Lu, Jin Li, Xintong Yu, Changshui Zhang and Qing Tao. (2018). Optimizing Top-k Multiclass SVM via Semismooth Newton Algorithm. IEEE Transactions on Neural Networks and Learning Systems, 2018 (Accepted, to appear).

Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang. (2017). Learning Efficient Convolutional Networks through Network Slimming. International Conference on Computer Vision (ICCV), (2017).

Rui Lu, Kailun Wu, Zhiyao Duan, Changshui Zhang. (2017). Deep ranking: triplet matchnet for music metric learning. Proc. of the Acoustics, Speech and Signal Processing (ICASSP) New Orleans, America. March, 2017.

Runpeng Cui, Hu Liu, Changshui Zhang. (2017). Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA. July, 2017.

Gong, P., Zhang, C., Lu, Z., Huang, J.Z., & Ye, J. (2013). A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. In Machine learning: Proceedings of the International Conference on Machine Learning (pp. 37). NIH Public Access.
Gong, P., Ye, J., & Zhang, C.S. (2012). Multi-stage multi-task feature learning. In Advances in Neural Information Processing Systems (pp. 1988-1996).
Gong, P., & Zhang, C. (2011). A fast dual projected Newton Method for L1-regularized least squares. In The 22nd International Joint Conference on Artificial Intelligenc (IJCAI) (pp. 1275-1280). IEEE.
He, J., & Zhang, J. (2014). In-hand haptic perception in dexterous manipulations. Science China Information Sciences, 57(12), 1-11.
Hu, X., Zhang, J., Li, J., & Zhang, B. (2014). Sparsity-regularized HMAX for visual recognition. PLOS ONE, 9(1), 1–12. 

Bai, T., Li, Y., & Zhou, X. (2014). Monocular human motion tracking with discriminative sparse representation. Advanced Robotics, 28(6), 403-414.
Pan, Z., & Zhang, C. (2015). Relaxed sparse eigenvalue conditions for sparse estimation via non-convex regularized regression. Pattern Recognition, 48(1), 231-243.
Pan, Z., Lin, M., Hou, G., Zhang, C. et al. (2015). Damping proximal coordinate descent algorithm for non-convex regularization. Neurocomputing, 152, 151-163.
Yu, J., Ding, R., Yang, Q., Tan, M., Wang, W., & Zhang, J. (2012). On a bio-inspired amphibious robot capable of multimodal motion. IEEE/ASME Transactions on Mechatronics, 17(5), 847-856.
Zhang, J., & Rössler, B. (2004). Self-valuing learning and generalization with application in visually guided grasping of complex objects. Journal of Robotics and Autonomous Systems, 47(2), 117-127.

    

Wieser, I., Barros, P., Heinrich, S., Wermter, S. (2018) Understanding Auditory Representations of Emotional Expressions with Neural Networks. Springer Journal Neural Computing and Applications.

Siqueira S., Sutherland A., Barros, P., Kerzel, M., Magg, S., Wermter, S. (2018) Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue. IEEE-RAS International Conference on Humanoid Robots (Humanoids), China.

Speck, D., Barros, P., Wermter, S. (2018) De-Noise-GAN: De-Noising Images to Improve RoboCup Soccer Ball Detection. Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN 2018), Greece.

Parisi, G. I., Barros, P., Fu, D., Magg, S., Liu, X., Wermter, S. (2018) A Neurorobotic Experiment for Crossmodal Conflict Resolution in Complex Environments. IEEE Conference on Intelligent Robots and Systems (IROS 2018).

Siqueira, H., Barros, P., Magg, S., Wermter, S., (2018) An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions . IEEE Conference on Intelligent Robots and Systems (IROS18).

Barros, P., Churamani, N., Lakomkin, E., Siqueira, H., Sutherland, A., Wertmer, S. (2018) The OMG-Emotion Behavior Dataset. IEEE WCCI/IJCNN’18, Rio de Janeiro, Brazil.

Fu, D., Barros, P., Parisi, G.I., Wu, H., Magg, S., Liu, X., Wermter, S. (2018). Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution. Crossmodal Learning for Intelligent Robotics Workshop, IEEE/RSJ IROS’ 18.

Siqueira, H., Barros, P., Magg, S., Weber, C., & Wermter, S. (2018). A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 1-6).

Barros, Parisi, Fu, Liu & Wermter (2018). Expectation Learning and Crossmodal Modulation with a Deep Adversarial Network. IEEE WCCI/IJCNN’18, Rio de Janeiro, Brazil. In press.

Mici, Parisi & Wermter (2018). An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction. IEEE Transactions on Cognitive and Developmental Systems. Accepted.

Cruz, Parisi & Wermter (2018). Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning. IEEE WCCI/IJCNN’18, Rio de Janeiro, Brazil. In press.

Churamani, Barros, Strahl & Wermter (2018). Learning Empathy-Driven Emotion Expressions using Affective Modulations. IEEE WCCI/IJCNN’18, Rio de Janeiro, Brazil. In press.

Parisi, Tani, Weber & Wemter (2017). Lifelong learning of human actions with deep neural network self-organization. Neural Networks (Elsevier), 96(137-149).

Barros, P., Parisi, G.I., Fu, D., Liu, X., Wermter S. (2017). Expectation Learning for Adaptive Crossmodal Stimuli Association, EUCog Meeting Proceedings, 2017.

Parisi, G.I., Barros, P., Kerzel, M., Wu, H., Yang, G., Li, Z., Liu, X., Wermter, S. (2017). A computational model of crossmodal processing for conflict resolution. Proc. International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB), pages 33-38.

Parisi, G.I., Tong, J., Barros, P., Roeder, B., Wermter, S (2017). Towards modeling the interplay of spatial-associative neural network representations for multisensory perception. Workshop on Computational models for crossmodal learning at ICDL-EPIROB.

Parisi, G.I., Barros, P., Wu, H., Yang, G., Li, Z., Liu, X., Wermter, S. (2017). A deep neural model for emotion-driven multimodal attention. AAAI Spring Symposium Series, pages 482-485.

Parisi, G.I., Wermter, S. (2017). Lifelong learning of action representations with deep neural self-organization. AAAI Spring Symposium Series, pages 608-612.

Barros, P., Parisi, G.I., Wermter, S. (2017). Emotion-modulated attention improves expression recognition: A deep learning model. Neurocomputing, Volume 253, pages 104-114.

Barros, P., & Wermter, S. (2017).A self-organizing model for affective memory. International Joint Conference on Neural Networks (IJCNN), pp. 31-38.

Churamani, N., Kerzel, M., Strahl, E., Barros, P., & Wermter, S. (2017). Teaching emotion expressions to a human companion robot using deep neural architectures. International Joint Conference on Neural Networks (IJCNN), pp. 627-634.

Tsironi, E., Barros, P., Weber, C., & Wermter, S. (2017). An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition. Neurocomputing.

Cruz, F., Parisi, G. I., Twiefel, J., & Wermter, S. (2016). Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario. 2016 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS), pp. 759-766.

Parisi, G.I., Tani, J., Weber, C., Wermter, S. (2016). Emergence of Multimodal Action Representations from Neural Network Self-Organization. Cognitive Systems Research

Parisi, G.I., Wermter, S. A Neurocognitive Robot Assistant for Robust Event Detection. (2016). Trends in Ambient Intelligent Systems: Role of Computational Intelligence, Series "Studies in Computational Intelligence", pp. 1-28, Springer, 2016

Cruz, F., Parisi, G. I., and Wermter, S. (2016). Learning Contextual Affordances with an Associative Neural Architecture. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 665-670, Bruges, Belgium, 2016.

Speck, D., Barros, P., Weber, C. and Wermter, S. (2016). Ball Localization for Robocup Soccer using Convolutional Neural Networks. RoboCup Symposium, Leipzig, Germany, 2016. - Best Paper Award

Barros, P. , Weber, C., Wermter, S. (2016). Learning Auditory Representations for Emotion Recognition. Proceedings of International Joint Conference on Neural Networks (IJCNN/WCCI), Vancouver, Canada, July 2016.

Mousavi, N., Siqueira, H., Barros, P., Fernandes, B., Wermter, S. (2016). Understanding How Deep Neural Networks Learn Face Expressions. Proceedings of International Joint Conference on Neural Networks (IJCNN/WCCI), Vancouver, Canada, July 2016.

Bauer, J., Dávila-Chacón, J., & Wermter, S. (2014). Modeling development of natural multi-sensory integration using neural self-organization and probabilistic population codes. Connection Science, 1-19.
Bauer, J., Magg, S., & Wermter, S. (2015). Attention modeled as information in learning multisensory integration. Neural Networks, 65, 44-52.
Dávila-Chacón, J., Magg, S., Liu, J., & Wermter, S. (2013). Neural and statistical processing of spatial cues for sound source localisation. In Proc. of Int. Joint Conf. on Neural Networks (pp. 1274-1281), IEEE.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2004). Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI. Neuroimage, 22(3), 1097-1106.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2006). Functional dissociation of attentional selection within PFC: response and non-response related aspects of attentional selection as ascertained by fMRI. Cereb Cortex, 16(6), 827-834.
Liu, X., Park, Y., Gu, X., & Fan, J. (2010). Dimensional overlap accounts for independence and integration of stimulus-response compatibility effects. Attention Perception & Psychophysics, 72(6), 1710-1720.
Murray, J., Erwin, H., & Wermter, S. (2009). Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks. Neural Networks, 22(2). 173–189.
Parisi, GI., Weber, C., &Wermter, S. (2015). Self-organizing neural integration of pose-motion features for human action recognition. Frontiers in Neurorobotics. from: http://journal.frontiersin.org/article/10.3389/fnbot.2015.00003/
Wermter, S., & Elshaw, M. (2003). Learning robot actions based on self-organising language memory. Neural Networks, 16(5), 691–699.
Zhong, J., Weber, C., & Wermter, S. (2013). A predictive network architecture for a robust and smooth robot docking behavior. Journal of Behavioral Robotics, 3(4), 172-180.

   

Publications project A6 - Brain-inspired multimodal deep learning

Publications of the current project