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Publications - Thematic Area A

Publications project A1 - Dynamic adaptation of multisensory processes by crossmodal recalibration

Publications of the current project

  • Bruns P, Li L, Guerreiro MJS, Shareef I, Rajendran SS, Pitchaimuthu K, Kekunnaya R, and Röder B (2022). Audiovisual spatial recalibration but not integration is shaped by early sensory experience, iScience 25, 104439, June 17, 2022. 10.1016/j.isci.2022.104439
  • Rohlf S, Bruns P, Röder B (2021). The effects of cue reliability on crossmodal recalibration in adults and children. Multisensory Research, 34, 743-761. https://doi.org/10.1163/22134808-bja10053
  • Bruns P, Dinse HR, Röder B (2020). Differential effects of the temporal and spatial distribution of audiovisual stimuli on cross-modal spatial recalibration. European Journal of Neuroscience, 52, 3763-3775. 10.1111/ejn.14779
  • Gudi-Mindermann H, Rimmele JM, Bruns P, Kloosterman NA, Donner TH, Engel AK, Röder B (2020). Post-training load-related changes of auditory working memory – An EEG study. Frontiers in Human Neuroscience, 14, 72. 10.3389/fnhum.2020.00072
  • Kramer A, Röder B, Bruns P (2020). Feedback modulates audio-visual spatial recalibration. Frontiers in Integrative Neuroscience, 13, 74. 10.3389/fnint.2019.00074
  • Li L, Rehr R, Bruns P, Gerkmann T, Röder B (2020). A survey on probabilistic models in human perception and machines. Frontiers in Robotics and AI, 7, 85. 10.3389/frobt.2020.00085
  • Rohlf S, Li L, Bruns P, Röder B (2020). Multisensory integration develops prior to crossmodal recalibration. Current Biology, 30, 1726-1732. 10.1016/j.cub.2020.02.048
  • Tong J, Li L, Bruns P, Röder B (2020). Crossmodal associations modulate multisensory spatial integration. Attention, Perception, & Psychophysics, 82, 3490-3506. 10.3758/s13414-020-02083-2

Publications of the phase-1 project

  • Bruns P (2019). The ventriloquist illusion as a tool to study multisensory processing: An update. Frontiers in Integrative Neuroscience, 13, 51. 10.3389/fnint.2019.00051
  • Bruns P, Röder B (2019). Visual recalibration of auditory spatial perception decays at different time scales. Proceedings of the International Congress on Acoustics, 2019, 3915-3920. 10.18154/RWTH-CONV-239152
  • Zierul B, Tong J, Bruns P, Röder B (2019). Reduced multisensory integration of self-initiated stimuli, Cognition, 182, 349-359.
  • Bruns P, Röder B (2019). 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.
  • Bruns P, Röder B (2019). Repeated but not incremental training enhances cross-modal recalibration. Journal of Experimental Psychology: Human Perception and Performance, 45(4), 435.
  • Badde S, Röder B, Bruns P (2018). Task-irrelevant sounds influence both temporal order and apparent motion judgments about tactile stimuli applied to crossed and uncrossed hands. Attention, Perception, & Psychophysics, 80(3), 773-783.
  • Gudi-Mindermann H, Rimmele JM, Nolte G, Bruns P, Engel AK, Röder B (2018). Working memory training in congenitally blind individuals results in an integration of occipital cortex in functional networks. Behavioural Brain Research, 348, 31-41. 10.1016/j.bbr.2018.04.002
  • Parisi GI, Tong J, Barros P, Röder B, Wermter S (2018). Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception. Workshop on Computational Models for Crossmodal Learning (ICDL-EpiRob).
  • Bruns P, Röder B, (2017). Spatial and frequency specificity of the ventriloquism aftereffect revisited. Psychological Research, 1-16.
  • 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. 10.1145/3131277.3132181
  • Zierul B, Röder B, Tempelmann C, Bruns P, 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. 10.1007/s00221-017-4907-6

Relevant previous work

  • 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.
  • 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.

Publications project A2 - Using multiperturbation analysis to reveal the neural circuit basis of multi-sensory processing

Publications of the current project

  • Chen QN, Ding XL, Guo XX, Zhou G, Guan JS (2022). Suv39h1 regulates memory stability by inhibiting the expression of Shank1 in hippocampal newborn neurons. Eur J Neurosci. 2022 Mar;55(6):1424-1441. 10.1111/ejn.15626 PMID: 35181969
  • Damicelli F, Hilgetag CC, Goulas A (2022). Brain connectivity meets reservoir computing. PLoS Comput Biol 18(11): e1010639. 10.1371/journal.pcbi.1010639
  • Fakhar K, Hilgetag CC (2022). Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. PLoS Comput Biol 18(6): e1010250. 10.1371/journal.pcbi.1010250
  • Fakhar K, Hadaeghi F, Hilgetag CC (2022). Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks, 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 10.1109/IJCNN55064.2022.9892782
  • Goulas A, Damicelli F, Hilgetag CC (2022). Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks. Neural Netw. 2021 Oct;142:608-618. 10.1016/j.neunet.2021.07.011 PMID: 34391175
  • Hilgetag CC, Goulas A, Changeux JP (2022). A natural cortical axis connecting the outside and inside of the human brain. Network Neuroscience, 6(4), 950-959, 2022. 10.1162/netn_a_00256
  • Lamothe-Molina PJ, Franzelin A, Beck L et al. (2022). ΔFosB accumulation in hippocampal granule cells drives cFos pattern separation during spatial learning. Nat Commun 13, 6376 (2022). 10.1038/s41467-022-33947-w
  • Li D, Wang G, Werner R, Xie H, Guan J-S and Hilgetag CC (2022). Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy. Front. Neuroinform. 15:674439. 10.3389/fninf.2021.6744
  • Liu KY, Li XY, Lai YR et al. (2022). Denoised Internal Models: A Brain-inspired Autoencoder Against Adversarial Attacks. Mach. Intell. Res. 19, 456–471 (2022). 10.1007/s11633-022-1375-7
  • Luo W, Yun D, Hu Y, Tian M, Yang J, Xu Y, Tang Y, Zhan Y, Xie H, Guan JS (2022). Acquiring new memories in neocortex of hippocampal-lesioned mice. Nat Commun. 2022 Mar 24;13(1):1601. 10.1038/s41467-022-29208-5 PMID: 35332120; PMCID: PMC8948206
  • Yan Y, Tian M, Li M, Zhou G, Chen Q, Xu M, Hu Y, Luo W, Guo X, Zhang C, Xie H, Wu QF, Xiong W, Liu S, Guan JS (2022). ASH1L haploinsufficiency results in autistic-like phenotypes in mice and links Eph receptor gene to autism spectrum disorder. Neuron 2022 Apr 6;110(7):1156-1172.e9. 10.1016/j.neuron.2021.12.035 PMID: 35081333
  • Zhang L, Kandil FI, Zhao K.,Fu X.,Lamm C., Hilgetag CC, Glaescher J (2022). A causal role of the human left temporoparietal junction in computing social influence during goal-directed learning, biorxiv: https://www.biorxiv.org/content/10.1101/2022.06.13.495824v1 10.1101/2022.06.13.495824
  • Beul SF, Goulas A, Hilgetag CC (2021). An architectonic type principle in the development of laminar patterns of cortico-cortical connections. Brain Struct Funct. 2021 May;226(4):979-987. 10.1007/s00429-021-02219-6 PMID: 33559742; PMCID: PMC8036174.
  • Changeux JP, Goulas A, Hilgetag CC (2021). A Connectomic Hypothesis for the Hominization of the Brain. Cereb Cortex. 2021 Mar 31;31(5):2425-2449. 10.1093/cercor/bhaa365 PMID: 33367521; PMCID: PMC8023825.
  • Goulas A, Changeux JP, Wagstyl K, Amunts K, Palomero-Gallagher N, Hilgetag CC (2021). The natural axis of transmitter receptor distribution in the human cerebral cortex. Proc Natl Acad Sci USA. 2021 Jan 19;118(3):e2020574118. 10.1073/pnas.2020574118 PMID: 33452137; PMCID: PMC7826352.
  • Jiang Y, Fu X, Zhang Y et al. (2021). Rett syndrome linked to defects in forming the MeCP2/Rbfox/LASR complex in mouse models. Nat Commun 12, 5767 (2021). 10.1038/s41467-021-26084-3
  • Wang G, Xie H, Hu Y, Chen Q, Liu C, Liu K, Yan Y, Guan JS (2021). Egr1-EGFP transgenic mouse allows in vivo recording of Egr1 expression and neural activity. J Neurosci Methods. 2021 Nov 1,363:109350. 10.1016/j.jneumeth.2021.109350 PMID: 34487772.
  • Beul SF, Hilgetag CC (2020). Systematic modelling of the development of laminar projection origins in the cerebral cortex: Interactions of spatio-temporal patterns of neurogenesis and cellular heterogeneity. PLoS computational biology. 2020 Oct 13;16(10):e1007991. 10.1371/journal.pcbi.1007991
  • Liu S, Tian M, He F et al. (2020). Spontaneous hyperactivity in Ash1l mutant mice, a new model for Tourette syndrome, Molecular Psychiatry (2020) 25:241–242. 10.1038/s41380-019-0642-7
  • Zhang X, Wang F, Hu Y, Chen R, Meng D, Guo L, Lv H, Guan J, Jia Y (2020). In vivo stress granule misprocessing evidenced in a FUS knock-in ALS mouse model. Brain. 2020 May 1;143(5):1350-1367. 10.1093/brain/awaa076 PMID: 32358598.

Publications of the phase-1 project

  • Damicelli F, Hilgetag CC, Hüt, MT, Messé A (2019). Topological reinforcement as a principle of modularity emergence in brain networks. Network Neuroscience, 3(2), 1-34.
  • Wang G, Xie H, Wang L, Luo W, Wang Y, Jiang J, ...Guan JS (2019). Switching From Fear to No Fear by Different Neural Ensembles in Mouse Retrosplenial Cortex. Cerebral Cortex pii: bhz050. 10.1093/cercor/bhz050
  • Li D, Zavaglia M, Wang G, Xie H, Hu Y, Werner R, ... Hilgetag CC (2019). Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning. Scientific Reports, 9(1), 7424.
  • Goulas A, Zilles K, Hilgetag CC. (2018). Cortical gradients and laminar projections in mammals. Trends in Neuroscience, 41(11), 775-788.
  • Luo W, Guan JS (2018). Do brain oscillations orchestrate memory? Brain Science Advances, 4(1), 16-33.
  • Jiang J, Wang G, Guan JS (2018). Mammillary body regulates state-dependent fear by alternating cortical oscillations Sci Rep, 8(1), 13471.
  • Messé A, Hütt MT, Hilgetag CC (2018). Toward a theory of coactivation patterns in excitable neural networks. PLoS Comput Biollgy, 14(4):e1006084.
  • Rollenhagen A, Ohana O, Sätzler K, Hilgetag CC, 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
  • 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
  • Malherbe C, Umarova R, Zavaglia M, Kaller C, Beume L, Thomalla G, Weiller C, Hilgetag C (2018). Neural correlates of visuospatial bias in patients with left hemisphere stroke: a causal functional contribution analysis based on game theory, Neuropsychologia, 115: 142-153.
  • 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.
  • 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
  • Goulas A, Uylings H, Hilgetag C (2017) Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse BRAIN STRUCT FUNCT. 222(3):1281-1295
  • 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. 20(5):690-699.
  • 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
  • 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.

Relevant previous work

  • Gräff J, Rei D, Guan JS, Wang WY, Seo J, Hennig KM, Nieland TJ, Fass DM, Kao PF, Kahn M, Su SC, Samiei A, Joseph N, Haggarty SJ, Delalle ., & Tsai LH (2012). An epigenetic blockade of cognitive functions in the neurodegenerating brain, Nature, 483:222.
  • Guan JS, Haggarty SJ, Giacometti E, Dannenberg JH, Joseph N, Gao J, Nieland TJ, Zhou Y, Wang X, Mazitschek R, Bradner JE, DePinho RA, Jaenisch R, & Tsai LH (2009). HDAC2 negatively regulates memory formation and synaptic plasticity, Nature, 459: 55.
  • Hilgetag CC, O'Neill MA, & Young MP (1996). Indeterminate organization of the visual system, Science, 271: 776.
  • Hilgetag CC, 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 CC (2004). Meilijson I, Ruppin E: Fair attribution of functional contribution in artificial and biological networks, Neural Computation, 16: 1887.
  • Keinan A, Sandbank B, & Hilgetag CC, Meilijson I, Ruppin E (2006). Axiomatic scalable neurocontroller analysis via the Shapley value, Artificial Life, 12: 333.
  • Sporns O, Chialvo D, Kaiser M, & Hilgetag CC (2004). Organization, development and function of complex brain networks, Trends in Cognitive Sciences, 8: 418-425.
  • Xie H, Liu Y, & Guan JS (2014). In vivo imaging of immediate early gene expression reveals layer-specific memory traces in the mammalian brain, PNAS, 111(7): 2788.
  • Young MP, Hilgetag CC, & 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 CC (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.

Publications project A3 - Multisensory integration in the aging brain — mechanisms and facilitation

Publications of the current project

  • Feng Junjiao et al. (2022). A cognitive neurogenetic approach to uncovering the structure of executive functions, Nature Communications, (2022) 13:4588. 10.1038/s41467-022-32383-0
  • Backhaus W, Braaß H, Higgen FL, Gerloff C and Schulz R (2021). Early parietofrontal network upregulation relates to future persistent deficits after severe stroke—a prospective cohort study, Brain Communications 2021. 10.1093/braincomms/fcab097
  • Higgen FL, Braaß H, Backhaus W, Schulz R, Xue G, Gerloff C (2021). Reduced frontal white matter microstructure in healthy older adults with low tactile recognition performance, Scientific Reports volume 11, Article number: 111689,. 10.1038/s41598-021-90995-w
  • Liu Chuqi, Zhifang Y, Chen C, Axmacher N, and Xue G (2021). Hippocampal Representations of Event Structure and Temporal Context during Episodic Temporal Order Memory, Cerebral Cortex, 2021;00: 1–15. 10.1093/cercor/bhab304
  • Sheng Jintao et al., (2021). The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases, NeuroImage 237 (2021) 118187. 10.1016/j.neuroimage.2021.118187
  • Feng J et al. (2020). Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns, Hum Brain Mapp. 2020;41:3305–331. 10.1002/hbm.25018
  • Misselhorn J, Göschl F, Higgen FL, Hummel FC, Gerloff C, Engel AK (2020). Sensory capability and information integration independently explain the cognitive status of healthy older adults. Sci Rep. 2020 Dec 31;10(1):22437. 10.1038/s41598-020-80069-8 PMID: 33384454; PMCID: PMC7775431
  • Higgen FL, Heine C, Krawinkel L, Göschl F, Engel AK, Hummel FC, Xue Gui, Gerloff C (2020). Crossmodal Congruency Enhances Performance of Healthy Older Adults in Visual-Tactile Pattern Matching. Frontiers in Aging Neuroscience 12, p. 74, 2020. 10.3389/fnagi.2020.00074
  • Higgen FL, Ruppel P, Görner M, Kerzel M, Hendrich N, Feldheim J, Wermter S, Zhang J and Gerloff C (2020). Crossmodal Pattern Discrimination in Humans and Robots: A Visuo-Tactile Case Study. Front. Robot. AI 7:540565. 10.3389/frobt.2020.540
  • Xiao Xiaoqian et al. (2020). Individual-specific and shared representations during episodic memory encoding and retrieval, NeuroImage 217 (2020) 11690, 10.1016/j.neuroimage.2020.116909
  • Ye Z, Shi L, Li A, Chen C, Xue G (2020). Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations, eLife 2020;9:e57023. 10.7554/eLife.57023

Publications of the phase-1 project

  • Higgen FL, Braaß H, Schulz R, Xue G, Gerloff C (2019). P35 A complex tactile recognition task as a marker for the aging brain. Clinical Neurophysiology 2019; 130(8): e162-e163. 10.1016/j.clinph.2019.04.688
  • Huang X, Zhang H, Chen C, Xue G, He Q (2019). The neuroanatomical basis of the Gambler’s fallacy: A univariate and multivariate morphometric study. Hum Brain Mapp 40, 967–975. https://doi.org/10.1002/hbm.24425
  • Zhu B, Chen C, Shao X, Liu W, Ye Z, Zhuang L, Zheng L, Loftus EF, Xue G (2019). Multiple interactive memory representations underlie the induction of false memory. Proceedings of the National Academy of Sciences U S A, 116, 3466-3475.
  • Cai, Y., Urgolites Z, Wood J, Chen C, Li S, Chen A, Xue G (2018). Distinct neural substrates for visual short-term memory of actions. Hum Brain Mapp 39, 4119–4133. https://doi.org/10.1002/hbm.24236
  • Xue G (2018). The Neural Representations Underlying Human Episodic Memory. Trends Cogn. Sci. (Regul. Ed.) 22, 544–561. doi: https://doi.org/10.1016/j.tics.2018.03.004
  • Higgen FL, Ruppel P, Görner M, Kerzel M, Magg S, Hendrich N (2018). Crossmodal pattern discrimination in humans and robots: a visuo-tactile case study. Workshop on Crossmodal Learning, IROS.2018.
  • Qu J, Qian L, Chen C, Xue G, Li H, Xie P, Mei L (2017). Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning. Front Hum Neurosci 11, 424. doi: https://doi.org/10.3389/fnhum.2017.00424

Relevant previous work

  • 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.

Publications project A4 - Crossmodal joint sparse feature learning in robot tasks

Publications of the current project

  • Ruppel P, Zhang J (2022). Efficent gradient propagation for robot control and learning. ICCCS 2022, Hengqin, Zhuhai, China.
  • Li L, Hu W, Lu J, Zhang J (2022). Leaf Vein Segmentation with Self-Supervision. Computer and Electronics in Agriculture, Vol 203, 107352, 2022. 10.1016/j.compag.2022.107352
  • Sun Y, Hu W, Zhou D, Mo B, Fu K, Che Z, Wang Z, Wang S, Zhao J, Ye J, Tang J, Zhang C (2022). Alleviating Data Sparsity Problems in Estimated Time of Arrival via Auxiliary Metric Learning, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 23231-23243, Dec. 2022. 10.1109/TITS.2022.3200445
  • Wang Y, Sun Y, Lei F, Zhang J (2022). Leveraging Sparse Coding for EEG Based Emotion Recognition in Shooting. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 10.1109/ICASSP43922.2022.9747506
  • Wuerkaixi A, Zhang Y, Duan Z, Zhang C (2022). Rethinking Audio-visual Synchronization for Active Speaker Detection. 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), 2022, pp. 01-06. 10.1109/MLSP55214.2022.9943352 code: https://paperswithcode.com/paper/rethinking-audio-visual-synchronization-for
  • Wuerkaixi A, Yan K, Zhang Y, Duan Z, Zhang C (2022). DyViSE: Dynamic Vision-Guided Speaker Embedding for Audio-Visual Speaker Diarization. 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), 2022, pp. 1-6, 10.1109/MMSP55362.2022.9948860
  • Ruppel P, Hendrich N, Zhang J (2021). Direct Policy Optimization with Differentiable Physical Consistency for Dexterous Manipulation. 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2021, pp. 650-655, 10.1109/ROBIO54168.2021.9739435
  • Zhong Cao, Sen Cui, Changshui Zhang (2021). DCR: Disentangled component representation for sketch generation. Pattern Recognition Letters. vol. 145, pp. 16-22. 2021.
  • Zhong Cao, Jiang Lu, Sen Cui and Changshui Zhang (2020). Zero-Shot Handwritten Chinese Character Recognition with Hierarchical Decomposition Embedding. Pattern Recognition vol. 107, Nov. 2020.
  • Yiwen Guo, Long Chen, Yurong Chen, and Changshui Zhang (2020). On Connections between Regularizations for Improving DNN Robustness. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
  • Yiwen Sun, Kun Fu, Zheng Wang, Changshui Zhang, and Jieping Ye (2021). Road Network Metric Learning for Estimated Time of Arrival. The 25th International Conference on Pattern Recognition (ICPR). Milan, Italy, 10-15 January, 2021.
  • Yiwen Sun, Yulu Wang, Kun Fu, Zheng Wang, Changshui Zhang, and Jieping Ye (2021). Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting. The 25th International Conference on Pattern Recognition (ICPR). Milan, Italy, 10-15 January, 2021.
  • Yiwen Guo, Ming Lu, Wangmeng Zuo, Changshui Zhang, Yurong Chen (2021). Deep Likelihood Network for Image Restoration with Multiple Degradation Levels. IEEE Transactions on Image Processing (2021).
  • Yiwen Sun, Yulu Wang, Kun Fu, Zheng Wang, Ziang Yan, Changshui Zhang, Jieping Ye (2021). FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention. The 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). June, 6-11, 2021.
  • Jiang Lu, Lei Li, and Changshui Zhang. Self-reinforcing Unsupervised Matching (2021). IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
  • Philipp Ruppel, Norman Hendrich, Jianwei Zhang (2021). Direct Policy Optimization with Differentiable Physical Consistency for Dexterous Manipulation. Proceedings IEEE ROBIO 2021.
  • Philipp Ruppel, Jianwei Zhang (2020). Learning Object Manipulation with Dexterous Hand-Arm Systems from Human Demonstration. IEEE/RSJ IROS 2020.
  • Niklas Fiedler, Philipp Ruppel, Yannick Jonetzko, Norman Hendrich, and Jianwei Zhang (2021). A Low-Cost Modular System of Customizable, Versatile, and Flexible Tactile Sensor Arrays. IEEE/RSJ IROS 2021.
  • Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang (2021). Towards Distraction-Robust Active Visual Tracking. ICML 2021.
  • Jing Xu, Fangwei Zhong, Yizhou Wang (2020). Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks. NeurIPS 2020.
  • Rongchang Xie, Chunyu Wang, Yizhou Wang (2020). MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation. CVPR 2020.
  • Focko L. Higgen, Philipp Ruppel, Michael Görner, Matthias Kerzel, Norman Hendrich, Jan Feldheim, Stefan Wermter, Jianwei Zhang, Christian Gerloff (2020). Crossmodal Pattern Discrimination in Humans and Robots: AVisuo-Tactile Case Study. Frontiers Robotics and AI, 2020. DOI:10.3389/frobt.2020.540565
  • Lin Cong, Michael Görner, Philipp Ruppel, Hongzhuo Liang, Norman Hendrich, Jianwei Zhang (2020). Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation. IROS 2020.
  • Yannick Jonetzko, Niklas Fiedler, Manfred Eppe, Jianwei Zhang (2020). Multimodal Object Analysis with Auditory and Tactile Sensing using Recurrent Neural Networks. ICCSIP 2020.

Publications of the phase-1 project

  • Liang, H., Li, S., Ma, X., Hendrich, N., Gerkmann T., Sun, F., & Zhang, J. (2019). Making sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring. International Conference on Intelligent Robots and Systems (IROS), Macau, China (in press).
  • Yu, X., Guo, T., Fu, K, Li, L, Zhang, C. and Zhang J. (2019). Image Captioning with Partially Rewarded Imitation Learning. International Joint Conference on Neural Network (IJCNN), Budapest, Hungary.
  • Tang, S., Ji, Y., Lyu, J., Mi, J., Li, Q., & Zhang, J. (2019). Visual Domain Adaptation Exploiting Confidence-Samples. International Conference on Intelligent Robots and Systems (IROS), Macau, China (in press).
  • Chang, D., Lin, M., & Zhang, C. (2018). On the Generalization Ability of Online Gradient Descent Algorithm Under the Quadratic Growth Condition. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 1-12.
  • Lu R., Duan Z. and Zhang C. (2018). Listen and Look: Audio–Visual Matching Assisted Speech Source Separation. IEEE Signal Processing Letters.
  • Lu, R., Duan, Z., & Zhang, C. (2018). Multi-Scale Recurrent Neural Network for Sound Event Detection. 2018 In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 131-135.
  • Lu, J., Li, J., Yan, Z., Mei, F., & Zhang, C. (2018). Attribute-Based Synthetic Network (ABS-Net): Learning more from pseudo feature representations. Pattern Recognition, 80, 129-142.
  • Lu, J., Cao, Z., Wu, K., Zhang, G., & Zhang, C. (2018). Boosting Few-Shot Image Recognition Via Domain Alignment Prototypical Networks. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 260-264.
  • Yan, C., Wu, K., & Zhang, C. (2018). A New Anchor-Labeling Method For Oriented Text Detection Using Dense Detection Framework. IEEE Signal Processing Letters, 25(9), 1295-1299.
  • Yan, C., Hu, J., & Zhang, C. (2018). Deep transformer: A framework for 2D text image rectification from planar transformations. Neurocomputing, 289, 32-43.
  • Chu, D., Lu, R., Li, J., Yu, X., Zhang, C., & Tao, Q. (2018). Optimizing Top-k Multiclass SVM via Semismooth Newton Algorithm. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6264-6275.
  • Fu, K., Li, J., Jin, J., & Zhang, C. (2018). Image-text surgery: Efficient concept learning in image captioning by generating pseudopairs. IEEE Transactions on Neural Networks and Learning Systems, (99), 1-12.
  • Yu, N., Hu, X., Song, B., Yang, J., & Zhang, J. (2018). Topic-Oriented Image Captioning Based on Order-Embedding. IEEE Transactions on Image Processing, 28(6), 2743-2754.
  • Ruppel, P., Jonetzko, Y., Görner, M., Hendrich, N., & Zhang, J.(2018). Simulation of the SynTouch BioTac Sensor. International Conference on Intelligent Autonomous Systems. DOI: 10.1007/978-3-030-01370-7_30
  • Ruppel, P., Hendrich, N., Starke, S., & Zhang, J. (2018). Cost functions to specify full-body motion and multi-goal manipulation tasks. In 2018 IEEE International Conference on Robotics and Automation (ICRA), 3152-3159.
  • Wu, K., & Zhang, C. (2018). Deep Generative Adversarial Networks for the Sparse Signal Denoising. In 2018 24th International Conference on Pattern Recognition (ICPR), 1127-1132.
  • Chu, D., Zhang, C., & Tao, Q. (2017). A faster cutting plane algorithm with accelerated line search for linear SVM. Pattern Recognition, 67, 127-138.
  • Liu, L., He, B., Zhuang, J., Zhang, L., & Lv, A. (2017). Force measurement system for invisalign based on thin film single force sensor. Measurement, 97, 1-7.
  • Hu, W., Zhuo, Q., Zhang, C., & Li, J. (2017). Fast branch convolutional neural network for traffic sign recognition. IEEE Intelligent Transportation Systems Magazine, 9(3), 114-126.
  • Lu, J., Hu, J., Zhao, G., Mei, F., & Zhang, C. (2017). An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 369-379.
  • Lu, R., Wu, K., Duan, Z., & Zhang, C. (2017, March). Deep ranking: Triplet matchnet for music metric learning. In 2017 IEEE International Confer ence on Acoustics, Speech and Signal Processing (ICASSP), 121-125.
  • Cui, R., Liu, H., & Zhang, C. (2017). Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7361-7369.
  • Tang, S., Chen, L., Mi, J., Ye, M., Li, Q., & Zhang, J. (2017). Adaptive pedestrian detection by modulating features in dynamical environment. IEEE International Conference on Robotics and Biomimetics (ROBIO), 62-67.
  • Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., & Zhang, C. (2017). Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision（ICCV）. 2736-2744.
  • Fu, K., Jin, J., Cui, R., Sha, F., & Zhang, C. (2016). 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), 2321-2334.
  • Guan, H., & Zhang, J. (2016). Multi-sensory based novel household object categorization system by using interactive behaviours. In 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1685-1690.
  • Zhang, L., He, B., Zhang, J., (2016). Multimodal Information Representation Based on Approximation Theory (in Chinese). Science Press, ISBN: 978-7-03-050253-7.

Relevant previous work

  • 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.

Publications project A5 - Crossmodal learning in a neurobotic cortical and midbrain model

Publications of the current project

  • Cao S, Fu D, Yang X, Barros P, Wermter S, Liu X, Wu H (2022). How Can AI Recognize Pain and Express Empathy, arXiv preprint arXiv:2110.04249. (in progress).
    https://doi.org/10.48550/arXiv.2110.04249 (preprint)
  • Chen C, Fu D, Liu X (2022). Better to misidentify than to miss: a review of occurrence mechanisms and applications of face pareidolia (in Chinese), Advances in Psychological Science, 2023, Vol. 31(2): 240-255.
    https://doi.org/10.3724/SP.J.1042.2023.00240
  • Duczek N, Kerzel M, Allgeuer P, Wermter S (2022). Self-organised Learning from Synthetic and Real Data for a Humanoid Exercise Robot, Frontiers in Robotics and AI 9:669719.
    https://doi.org/10.3389/frobt.2022.6697
  • Eppe M, Gumbsch C, Kerzel M et al. (2022). Intelligent problem-solving as integrated hierarchical reinforcement learning, Nature Machine Intelligence 4, 11–20 (2022).
    https://doi.org/10.1038/s42256-021-00433-9
  • Fu D, Abawi F, Carneiro H, Kerzel M, Chen Z, Strahl E, Liu X, Wermter S (2022). A trained humanoid robot can perform human-like crossmodal social attention conflict resolution, International Journal of Social Robotics. (in press).
    https://doi.org/10.48550/arXiv.2111.01906
  • Fu D, Abawi F, Strahl E, Wermter S (2022). Judging by the look: The impact of robot gaze strategies on human cooperation., IEEE RO-MAN 2022, Workshop on Machine Learning for HRI: Bridge the Gap between Action and Perception.
    https://doi.org/10.48550/arXiv.2208.11647
  • Qu L, Weber C, Wermter S (2022). LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip, IEEE Trans Neural Netw Learn Syst. 2022.
    https://doi.org/10.1109/TNNLS.2022.3191677.
  • Zhao X, Weber C, Hafez MB, Wermter S (2022). Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 2512-2518.
    https://doi.org/10.1109/IROS47612.2022.9981510
  • Cao, S., Fu, D., Yang, X., Barros, P., Wermter, S., Liu, X., & Wu, H. (2021). How Can AI Recognize Pain and Express Empathy. arXiv preprint arXiv:2110.04249. (under review)
  • Duczek, N., Kerzel, M., & Wermter, S. (2021). Continual Learning from Synthetic Data for a Humanoid Exercise Robot. arXiv preprint arXiv:2102.10034. (under review)
  • Fu, D., Abawi, F., Carneiro, H., Kerzel, M., Chen, Z., Strahl, E., Liu, X., & Wermter, S. (2021). A trained humanoid robot can perform human-like crossmodal social attention conflict resolution. https://arxiv.org/pdf/2111.01906.pdf (under review)
  • Barros, P., Barakova, E., & Wermter, S. (2020). Adapting the Interplay between Personalized and Generalized Affect Recognition based on an Unsupervised Neural Framework. IEEE Transactions on Affective Computing.
  • Wieser, I., Barros, P., Heinrich, S., & Wermter, S. (2020). Understanding Auditory Representations of Emotional Expressions with Neural Networks. Neural Computing and Applications, 32(4), 1007–1022.
  • Mohammadi, H. B., Xirakia, N., Abawi, F., … & Wermter, S. (2019). Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario. Proceedings of The IEEE International Conference on Robotics and Automation ICRA-2019, 4317–4324.
  • Qu, L., Weber, C., & Wermter, S. (2019). LipSound: Neural Mel-spectrogram Reconstruction for Lip Reading. Proceedings of Interspeech2019.
  • Alpay, T., Abawi, F., & Wermter, S. (2019). Preserving Activations in Recurrent Neural Networks Based on Surprisal. Neurocomputing, 342, 75–82.
  • Carneiro, H., Weber, C., & Wermter, S. (2021). FaVoA: Face-Voice Association Favours Ambiguous Speaker Detection. Proceedings of The Thirtieth International Conference on Artificial Neural Networks ICANN-2021, 439-450.
  • Abawi, F., Weber, T., & Wermter, S. (2021). GASP: Gated Attention for Saliency Prediction. Proceedings of The Thirtieth International Joint Conference on Artificial Intelligence IJCAI-2021, 584-591.
  • Fu, D., Weber, C., Yang, G., Kerzel, M., Nan, W., Barros, P., ... & Wermter, S. (2020). What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective.  Frontiers in Integrative Neuroscience, 14, 10.
  • Ahrens, K., Abawi, F., & Wermter, S. (2021). Dynamic Representations for Imbalanced Lifelong Learning. Proceedings of The Thirtieth International Conference on Artificial Neural Networks ICANN-2021, 409-420.
  • Fang, H., Carbajal, G., Wermter, S., & Gerkmann, T. (2021). Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 676-680.
  • Andriella, A., Siqueira, H., Fu, D., Magg, S., Barros, P., Wermter, S., ... & Alenya, G. (2020). Do I have a personality? Endowing care robots with context-dependent personality traits.  International Journal of Social Robotics, 1-22.
  • Higgen, F. L., Ruppel, P., Görner, M., Kerzel, M., Hendrich, N., Feldheim, J., ... & Gerloff, C. (2020). Crossmodal pattern discrimination in humans and robots: a visuo-tactile case study. Frontiers in Robotics and AI, 7.
  • Qu, L., Weber, C., & Wermter, S. (2020). Multimodal Target Speech Separation with Voice and Face References. Proceedings of Interspeech2020.
  • Weber, T., & Wermter, S. (2020). Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction. AI-HRI Symposium at AAAI-FSS.
  • Kerzel, M., Abawi, F., Eppe, M., & Wermter, S. (2020). Enhancing a Neurocognitive Shared Visuomotor Model for Object Identification, Localization, and Grasping With Learning From Auxiliary Tasks. IEEE Transactions on Cognitive and Developmental Systems, 1–13.

Publications of the phase-1 project

  • Marzouk, A., Barros, P., Eppe, M., & Wermter, S. (2019). The Conditional Boundary Equilibrium Generative Adversarial Network and its Application to Facial Attributes. Proceedings of The International Joint Conference on Neural Networks IJCNN-2019, 1–7.
  • Barros, P., Fliesswasser, E., Kerzel, M., & Wermter, S. (2019). Exploring Low-level and High-level Transfer Learning for Multi-task Facial Recognition with a Semi-supervised Neural Network. Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems IROS-2019, 1378–1384.
  • Mici, L., Parisi, G.I., & Wermter, S. (2019). Compositional Learning of Human Activities With a Self-Organizing Neural Architecture. Frontiers in Robotics and AI, 6.
  • Barros, P., Churamani, N., Lim, A., & Wermter, S. (2019). The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling. Proceedings of The Eighth IEEE International Conference on Affective Computing and Intelligent Interaction ACII-2019, 1–7.
  • Barros, P., Eppe, M., Parisi, G. I., Liu, X., & Wermter, S. (2019). Expectation Learning for Stimulus Prediction Across Modalities Improves Unisensory Classification. Frontiers in Robotics and AI, 6, 137.
  • Barros, P., Wermter, S., & Sciutti, A. (2019). Towards Learning How to Properly Play UNO with the iCub Robot. In Proceedings of the ICDL-EPIROB Workshop on Naturalistic Non-verbal and Affective Human-Robot Interactions. arXiv:1908.00744.
  • Barros, P., Parisi, G., & Wermter, S. (2019). A Personalized Affective Memory Model for Improving Emotion Recognition. In International Conference on Machine Learning (pp. 485-494).
  • Parisi, G., Barros, P., Fu, D., Magg, S., Wu, H., Liu, X., & Wermter, S. (2019). A Neurorobotic Experiment for Crossmodal Conflict Resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 33-36).
  • Lindt, A., Barros, P., Siqueira, H., & Wermter, S. (2019). Facial Expression Editing with Continuous Emotion Labels. In 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) (pp. 1-8). IEEE.
  • Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks.113，54-71.
  • Barros, P., Parisi, G. I., Fu, D., Liu, X., & Wermter, S. (2018). Expectation learning for adaptive crossmodal stimuli association. EUCognition Meeting, arXiv preprint arXiv:1801.07654.
  • 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. arXiv preprint arXiv:1810.06748.
  • Barros, P., Parisi, G. I., Fu, D., Liu, X., & Wermter, S. (2018). Expectation Learning and Crossmodal Modulation with a Deep Adversarial Network. In 2018 International Joint Conference on Neural Networks (IJCNN), 1-8.
  • Mici, L., Parisi, G. I., & Wermter, S. (2018). An incremental self-organizing architecture for sensorimotor learning and prediction. IEEE Transactions on Cognitive and Developmental Systems, 10(4), 918-928
  • Cruz, F., Parisi, G. I., & Wermter, S. (2018). Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning. In 2018 International Joint Conference on Neural Networks (IJCNN), 1-8.
  • Churamani, N., Barros, P., Strahl, E., & Wermter, S. (2018). Learning empathy-driven emotion expressions using affective modulations. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8).
  • Parisi, G. I., Tong, J., Barros, P., Röder, B., & Wermter, S. (2018). Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception. Workshop on Computational Models for Crossmodal Learning (ICDL-EpiRob).
  • Parisi, G. I., Barros, P., Fu, D., Magg, S., Wu, H., Liu, X., & Wermter, S. (2018). A neurorobotic experiment for crossmodal conflict resolution in complex environments. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2330-2335.
  • 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., Wermter, S. (2017). Lifelong learning of action representations with deep neural self-organization. AAAI Spring Symposium Series, pages 608-612.
  • 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.
  • 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.
  • Parisi, G.I., Tani, J., Weber, C., Wermter, S. (2017). Emergence of Multimodal Action Representations from Neural Network Self-Organization. Cognitive Systems Research
  • 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.
  • 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., & 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), 665-670, Bruges, Belgium.
  • 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.

Relevant previous work

  • 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

  • Wang Z, Wang K, Hu X (2022). Accelerating Allen Brain Institute’s Large-Scale Computational Model of Mice Primary Visual Cortex., CAAI International Conference on Artificial Intelligence 2022, pp. 610-614.
    https://doi.org/10.1007/978-3-031-20503-3_57
  • Wang J, Hu X (2021). Convolutional neural networks with gated recurrent connections, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 44, pp 3421-3435.
    https://doi.org/10.1109/TPAMI.2021.3054614
  • Hu X, Zeng Z (2022). Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding., Neural Computation. 2022 Jan 1;34(1):104-37.
    https://doi.org/10.1162/neco_a_01453
  • Zhao Z, Liu Y, Zhang G, Tang L, Hu X (2022). The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image, 2022 14th International Conference on Advanced Computational Intelligence (ICACI), 2022, pp. 376-380.
    https://doi.org/10.1109/ICACI55529.2022.9837765
  • Hu Z, Huang S, Zhu X, Sun F, Zhang B, Hu X (2022). Adversarial Texture for Fooling Person Detectors in the Physical World., Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022, pp. 13307-13316.
    https://doi.org/10.1109/CVPR52688.2022.01295
  • Hu Z, Zhu J, Zhang B, Hu X (2022). Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness, Neurocomputing. 2022 Sep 21;505:142-53.
    https://doi.org/10.1016/j.neucom.2022.07.018
  • Tang C, Xie L, Zhang G, Zhang X, Tian Q, Hu X (2022). Active Pointly-Supervised Instance Segmentation, European Conference on Computer Vision 2022, pp. 606-623.
    https://doi.org/10.1007/978-3-031-19815-1_35
  • Hu X, Tang C, Chen H, Li X, Li J, Zhang Z (2022). Improving Image Segmentation with Boundary Patch Refinement, International Journal of Computer Vision. 2022 Nov;130(11):2571-89.
    https://doi.org/10.1007/s11263-022-01662-0
  • Chen H, Tang C, Hu X (2022). Dense Contrastive Loss for Instance Segmentation, British Machine Vision Conference (BMVC) 2022.
  • Zhu X, Hu Z, Huang S, Li J, Hu X (2022). Infrared Invisible Clothing: Hiding from Infrared Detectors at Multiple Angles in Real World, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022, pp. 13317-13326.
    https://doi.org/10.1109/CVPR52688.2022.01296
  • Kuo TY, Liao Y, Li K, Hong B, Hu X (2022). Inferring Mechanisms of Auditory Attentional Modulation with Deep Neural Networks., Neural Computation 34(11):2273-93.
    https://doi.org/10.1162/neco_a_01537
  • Chen H, Li J, Frintrop S, Hu X (2022). The MSR-Video to Text dataset with clean annotations, Computer Vision and Image Understanding. 2022 Dec 1;225:103581.
    https://doi.org/10.1016/j.cviu.2022.103581
  • Hu Z, Huang S, Zhu X, Sun F, Zhang B, Hu X (2022). Adversarial Texture for Fooling Person Detectors in the Physical World, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 13307-13316.
    https://doi.org/10.1109/CVPR52688.2022.01295
  • Hu X, Zeng Z, Neural Computation (2022). Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding, Neural Computation 34, 104–137 (2022).
    https://doi.org/10.1162/neco_a_01453
  • Hu Z, Zhu J, Zhang B, Hu X (2022). Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness, Neurocomputing. 2022 Sep 21;505:142-53.
    https://doi.org/10.1016/j.neucom.2022.07.018
  • Richter J, Liebold J, Gerkmann T (2022). Continuous Phoneme Recognition based on Audio-Visual Modality Fusion, 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1-8.
    https://doi.org/10.1109/IJCNN55064.2022.9892053
  • Welker S, Richter J, Gerkmann T (2022). Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain, Proc. Interspeech 2022, 2928-2932.
    https://doi.org/10.21437/Interspeech.2022-10653
  • Zhu X, Hu Z, Huang S, Li J, Hu X (2022). Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 13317-13326.
    https://doi.org/10.1109/CVPR52688.2022.01296
  • Banik S, Lauri M, Knoll A, Frintrop S (2021). Object Localization with Attribute Preference Based on Top-Down Attention., International Conference on Computer Vision Systems 2021 (ICCVS), pp. 28-40.
    https://doi.org/10.1007/978-3-030-87156-7_3
  • Xiaolin Hu, Zhigang Zeng, “Bridging the functional and wiring properties of V1 neurons through sparse coding, Neural Computation. 1-34, 2021. doi 10.1162/neco_a_01453
  • Jianjin Xu, Zheyang Xiong, Xiaolin Hu (2021). Frame difference-based temporal loss for video stylization, preprint, arXiv:2102.05822, 2021.
  • Xiao Li, Jianmin Li, Ting Dai, Jie Shi, Jun Zhu, Xiaolin Hu (2021). Rethinking Natural Adversarial Examples for Classification Models, preprint arXiv:2102.1173.
  • Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu (2021). RefineMask: Towards high-quality instance segmentation with fine-grained features, Proc. of the 34th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 19-25, 2021.
  • Chufeng Tang, Hang Chen, Xiao Li, Jianmin Li, Zhaoxiang Zhang, Xiaolin Hu (2021). Look closer to segment better: boundary patch refinement for instance segmentation, Proc. of the 34th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 19-25, 2021.
  • Han Liu, Shifeng Zhang, Ke Lin, Jing Wen, Jianmin Li, Xiaolin Hu (2021). Vocabulary-wide credit assignment for training image captioning models, IEEE Transactions on Image Processing, vol. 30, pp. 2450-2460, 2021.
  • Xiaopei Zhu, Xiao Li, Jianmin Li, Zheyao Wang, Xiaolin Hu (2021). Fooling thermal infrared pedestrian detectors in real world using small bulbs, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Virtual, Feb 2-9, 2021.
  • Haoran Chen, Jianmin Li, Simone Frintrop, and Xiaolin Hu (2021). Annotation cleaning for the MSR-Video to Text dataset, arXiv:2102.06448, 2021 (under review).
  • Hu, Li, Zhang, Luo, Lemercier, Gerkmann (2021). Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network, NeurIPS, Dec. 2021.
  • Ge Gao, Mikko Lauri, Xiaolin Hu, Jianwei Zhang, Simone Frintrop (2021). CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds, Proceedings of the International Conference on Robotics and Automation (ICRA) 2021.
  • Haoran Chen, Ke Lin, Alexander Maye, Jianming Li, and Xiaolin Hu (2020). A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling, Frontiers in Robotics and AI, September 30, 2020
  • Ge Gao, Mikko Lauri, Yulong Wang, Xiaolin Hu, Jianwei Zhang and Simone Frintrop. (2020). 6D Object Pose Regression via Supervised Learning on Point Clouds. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2020, Paris, France. [Arxiv preprint 2001.08942], [Code].
  • Carbajal, Richter, and Gerkmann (2021). Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier, IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP-2021), Toronto, Ontario, Canada, Jun. 2021.
  • Caus, Carbajal, Gerkmann, Frintrop (2021). See the silence: improving visual-only voice activity detection by optical flow and RGB fusion, Int. Conference on Computer Vision Systems (ICVS-2021), Sep. 2021.
  • Carbajal, Richter, and Gerkmann (2021). Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2021.
  • Fang, Carbajal, Wermter, Gerkmann (2021). Variational Autoencoder for Speech Enhancement With a Noise-Aware Encoder, IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP-2021), Toronto, Ontario, Canada, Jun. 2021.
  • Fang, Carbajal, Wermter, Gerkmann (2021). Joint Reduction of Ego-noise and Environmental Noise with a Partially-adaptive Dictionary, ITG Conference on Speech Communication, Kiel, Germany, Sep. 2021.
  • Julius Richter, Guillaume Carbajal, Timo Gerkmann (2020). Speech Enhancement with Stochastic Temporal Convolutional Networks", ISCA Interspeech, Shanghai, China, Oct. 2020.
  • Nguyen, Richter, Lauri, Gerkmann, Frintrop (2020). Improving mix-and-separate training in audio-visual sound source separation with an object prior, ICPR 2020.
  • Li, Rehr, Bruns, Gerkmann, Röder (2020). A survey on probabilistic models in human perception and machines, Frontiers in Robotics and AI, Vol. 7, No. 85, pp. 1-9, Jul. 2020.
  • Liang, Zhou, Li, Ma, Hendrich, Gerkmann, Sun, Stoffel, Zhang (2020). Robust Robotic Pouring Using Audition and Haptics, IEEE/RSJ IROS 2020, Las Vegas, USA, Oct 2020.

Publications from phase 1

  • Feng, W., Liu, W., Li, T., Peng, J., Qian, C., & Hu, X. (2019). Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Honolulu, Hawaii, USA.
  • Yu, M., Weber, C., Hu, X. (2019). Learning Sparse Hidden States in Long Short-Term Memory. In Proc. of the International Conference on Artificial Neural Networks (ICANN2019)
  • Liao, F., Liang, M., Li, Z., Hu, X., & Song, S. (2019). Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-or Network. IEEE transactions on neural networks and learning systems.
  • Liu, W., Chen, J., Li, C., Qian, C., Chu, X., & Hu, X. (2018). A Cascaded Inception of Inception Network with Attention Modulated Feature Fusion for Human Pose Estimation. The Thirty-Second AAAI Conference on Artificial Intelligence.
  • Xiong, Z., Weber, C., & Hu, X. (2018). Frame Difference-Based Real-Time Video Stylization in Video Calls. In 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP), 333-339.
  • Wang, Y., Su, H., Zhang, B., & Hu, X. (2018). Interpret neural networks by identifying critical data routing paths. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8906-8914.
  • Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., & Zhu, J. (2018). Defense against adversarial attacks using high-level representation guided denoiser. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1778-1787.
  • Springenberg, S., Lakomkin, E., Weber, C., & Wermter, S. (2018). Image-to-Text Transduction with Spatial Self-Attention. In 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).
  • Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., & Li, J. (2018). Boosting adversarial attacks with momentum. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9185-9193).
  • Sun, T., Wang, Y., Yang, J., Hu, X., (2017). Convolutional neural networks with two pathways for image style recognition, IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4102-4113, 2017.
  • Wang, J., & Hu, X. (2017). Gated recurrent convolution neural network for ocr. In International Conference on Advances in Neural Information Processing Systems(NeurIPS), 335-344.
  • Hu, Z., Wang. T., Hu, X., (2017). An STDP-based supervised learning algorithm for spiking neural networks, Proc. of 24th International Conference on Neural Information Processing (ICONIP), Guangzhou, China, Nov. 14-18, 2017.
  • Hu, X., Wang, T., (2017). Training the Hopfield Neural Network for Classification Using a STDP-Like Rule, Proc. of 24th International Conference on Neural Information Processing (ICONIP), Guangzhou, China, Nov. 14-18, 2017.
  • Yu, N., Qiu, S., Hu, X., Li, J., (2017). Accelerating Convolutional Neural Networks by Group-wise 2D-filter Pruning, Proc. of International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska, USA, May 14–19, 2017, pp. 2502-2509.
  • Wu, J., Ma, L., Hu, X., (2017). Delving deeper into convolutional neural networks for camera relocalization, Proc. of IEEE International Conference on Robotics and Automation (ICRA), Singapore, May 29- June 3, 2017, pp. 5644-5651.
  • Zhao, Y., Jin, X., Hu, X., (2017). Recurrent convolutional neural network for speech processing, Proc. of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March 5-9, 2017.
  • Zhou, X., Weber, C., Wermter, S. (2017). Robot Localization and Orientation Detection based on Place Cells and Head-direction Cells. Proceedings of the 26th International Conference on Artificial Neural Networks (ICANN 2017), Sep 2017.
  • Zhuang, C., Wang, Y., Yamins, D., & Hu, X. (2017). Deep learning predicts correlation between a functional signature of higher visual areas and sparse firing of neurons. Frontiers in Computational Neuroscience, 11, 100.
  • Chen, X., Hu, X., Zhou, H., & Xu, N. (2017, May). Fxpnet: Training a deep convolutional neural network in fixed-point representation. In 2017 International Joint Conference on Neural Networks (IJCNN), 2494-2501.
  • Wu, J., Ma, L., Hu, X., (2016). Predicting world coordinates of pixels in RGB images using convolutional neural network for camera relocalization, Proc. of the Seventh International Conference on Intelligent Control and Information Processing (ICICIP), Siem Reap, Cambodia, December 1-4, 2016.
  • Qin H., Yan J., Li X., Hu X., (2016). Joint Training of Cascaded CNN for Face Detection, Proc. of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3456-3465.

Relevant previous work

  • Heinrich, S., Weber, C., & Wermter, S. (2013). Embodied language understanding with a multiple timescale recurrent neural network. In Artificial Neural Networks and Mahine Learning-ICANN 2013 (pp. 216-223). Springer Berlin Heidelberg.
  • Hu, X., Zhang, J., Qi, P., & Zhang, B. (2014). Modeling response properties of V2 neurons using a hierarchical K-means model. Neurocomputing, 134, 198-205.
  • Hu, X., Sun, C., & Zhang, B. (2010). Design of recurrent neural networks for solving constrained least absolute deviation problems. IEEE Transactions on Neural Networks, 21(7), 1073-1086.
  • Hu, X., & Zhang, B. (2009). An alternative recurrent neural network for solving variational inequalities and related optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(6), 1640-1645.
  • Hu, X., & Zhang, B. (2010). A Gaussian attractor network for memory and recognition with experience-dependent Learning. Neural Computation, 22(5), 1333-1357.
  • Liang, M., & Hu, X. (2015). Predicting eye fixations with higher-level visual features. IEEE Transactions on Image Processing, 24(3), 1178-1189.
  • Liang, M., & Hu, X. (2015). Recurrent convolutional neural network for object recognition. In Proc. of the 28th IEEE Conference on Computer Vision and Pattern Recognition (pp. 3367-3375). IEEE.
  • Weber, C., & Triesch, J. (2008). A sparse generative model of V1 simple cells with intrinsic plasticity. Neural Computation, 20(5), 1261-84.
  • Wu, Y., Liu, Y., Li, J., Liu, H., & Hu, X. (2013). Traffic sign detection based on convolutional neural networks. In the 2013 International Joint Conference on Neural Network (IJCNN) (pp. 1-7). IEEE.
  • Zhong, J., Weber, C., & Wermter, S. (2013). A predictive network architecture for a robust and smooth robot docking behavior. Paladyn, Journal of Behavioral Robotics, 3(4), 172-180.