{"id":18,"date":"2023-05-12T16:02:27","date_gmt":"2023-05-12T16:02:27","guid":{"rendered":"https:\/\/heikohoffmann.de\/?page_id=18"},"modified":"2026-01-17T06:53:51","modified_gmt":"2026-01-17T06:53:51","slug":"publications","status":"publish","type":"page","link":"https:\/\/heikohoffmann.de\/index.php\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Google Scholar Citations<\/a><\/p>\n\n\n\n

Peer-reviewed journal articles<\/strong><\/h5>\n\n\n\n
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  1. Model for computing with population-encoded variables explains neural correlations<\/em>. Heiko Hoffmann<\/strong>. Network: Computation in Neural Systems (accepted for publication)<\/li>\n\n\n\n
  2. Computational model of layer 2\/3 in mouse primary visual cortex explains observed visuomotor mismatch response<\/em>. Heiko Hoffmann<\/strong>. Journal of Computational Neuroscience, 52<\/strong>, 323\u2013329, 2024. [pdf<\/a>, 2.1M] The Version of Record is available online at: https:\/\/doi.org\/10.1007\/s10827-024-00882-2<\/a><\/li>\n\n\n\n
  3. Sparse Associative Memory<\/em>. Heiko Hoffmann. <\/strong>Neural Computation<\/a>, Vol. 31(5), pp. 998-1014, 2019. Available online at The MIT Press<\/a> [pdf<\/a>, 584K] <\/li>\n\n\n\n
  4. Impact of network topology on self-organized criticality<\/em>. Heiko Hoffmann<\/strong>. Physical Review E<\/a>, Vol. 97, 022313, 2018 [pdf<\/a>, 719K] <\/li>\n\n\n\n
  5. Optimization by self-organized criticality<\/em>. <\/strong>Heiko Hoffmann<\/strong> and David W Payton. Scientific Reports, Vol. 8, Article no. 2358, 2018. Available online at nature.com<\/a> [pdf<\/a>, 2.6M]  <\/li>\n\n\n\n
  6. Suppressing cascades in a self-organized-critical model with non-contiguous spread of failures<\/em>. Heiko Hoffmann<\/strong> and David W Payton. Chaos, Solitons & Fractals<\/a>, Vol. 67, pp. 87-93, 2014. Available online at ScienceDirect<\/a> [pdf<\/a>, 644K] <\/li>\n\n\n\n
  7. Adaptive robotic tool use under variable grasps<\/em>. Heiko Hoffmann<\/strong>, Zhichao Chen, Darren Earl, Derek Mitchell, Behnam Salemi, and Jivko Sinapov. Robotics and Autonomous Systems<\/a>, Vol. 62, Issue 6, pp. 833-846, 2014. Available online at ScienceDirect<\/a> [pdf<\/a>, 2.4M] <\/li>\n\n\n\n
  8. Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors<\/em>. Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann<\/strong>, Peter Pastor, and Stefan Schaal. Neural Computation<\/a>, Vol. 25, No. 2, pp. 328-373, 2013. [pdf<\/a>, 3.2M] Cited by 1,700+<\/a> (most cited article in Neural Computation since 2010) <\/li>\n\n\n\n
  9. Target switching in curved human arm movements is predicted by changing a single control parameter<\/em>. Heiko Hoffmann<\/strong>. Experimental Brain Research<\/a>, Vol. 208, Issue 1, pp. 73-87, 2011. Available online at springerlink.com<\/a> [pdf<\/a>, 636K] <\/li>\n\n\n\n
  10. Grasping to Extrafoveal Targets: A Robotic Model<\/em>. Wolfram Schenck, Heiko Hoffmann<\/strong>, and Ralf M\u00f6ller. New Ideas in Psychology<\/a>, Vol. 29, pp. 235-259, 2011. Available online at ScienceDirect<\/a> [pdf<\/a>, 984K] <\/li>\n\n\n\n
  11. Computational models for neuromuscular function<\/em>. Francisco Valero-Cuevas, Heiko Hoffmann<\/strong>, Manish Kurse, Jason Kutch, and Evangelos Theodorou (all authors contributed equally). IEEE Reviews in Biomedical Engineering<\/a>, Vol. 2, pp. 110-135, 2009. [pdf<\/a>, 1.6M] <\/li>\n\n\n\n
  12. Local dimensionality reduction for non-parametric regression<\/em>. Heiko Hoffmann<\/strong>, Stefan Schaal, and Sethu Vijayakumar. Neural Processing Letters<\/a>, Vol. 29, pp. 109-131, 2009. The original article is available online at Springerlink.com<\/a> [pdf<\/a>, 556K] <\/li>\n\n\n\n
  13. Perception through Visuomotor Anticipation in a Mobile Robot<\/em>. Heiko Hoffmann<\/strong>. Neural Networks<\/a>, Vol. 20, pp. 22-33, 2007. Available online at ScienceDirect<\/a> [pdf<\/a>, 1564K] <\/li>\n\n\n\n
  14. Kernel PCA for Novelty Detection<\/em>. Heiko Hoffmann<\/strong>. Pattern Recognition<\/a>, Vol. 40, pp. 863-874, 2007. Available online at ScienceDirect<\/a> [pdf<\/a>, 748K] [code<\/a>] Cited by 900+<\/a><\/li>\n\n\n\n
  15. Learning visuomotor transformations for gaze-control and grasping<\/em>. Heiko Hoffmann<\/strong>, Wolfram Schenck, Ralf M\u00f6ller. Biological Cybernetics<\/a>, Vol. 93, pp. 119-130, 2005. Available online at springerlink.com<\/a> [pdf<\/a>, 620K] <\/li>\n\n\n\n
  16. Studies of crack dynamics in clay soil. I. Experimental methods, results and morphological quantification<\/em>. Hans-J\u00f6rg Vogel, Heiko Hoffmann<\/strong>, Kurt Roth. Geoderma<\/a>, Vol. 125, pp. 203-211, 2005. [pdf<\/a>, 576K] <\/li>\n\n\n\n
  17. Studies of crack dynamics in clay soil. II. A physically based model for crack formation<\/em>. Hans-J\u00f6rg Vogel, Heiko Hoffmann<\/strong>, Andreas Leopold, Kurt Roth. Geoderma<\/a>, Vol. 125, pp. 213-223, 2005. [pdf<\/a>, 596K] <\/li>\n\n\n\n
  18. An extension of Neural Gas to local PCA<\/em>. <\/a>Ralf M\u00f6ller, Heiko Hoffmann<\/strong>. Neurocomputing<\/a>, Vol. 62, pp. 305-326, 2004. [pdf<\/a>, 1194K] <\/li>\n\n\n\n
  19. Cross-correlation studies with seismic noise<\/em>. Heiko Hoffmann<\/strong>, John Winterflood, Yong-Jing Cheng, David G. Blair. Classical and Quantum Gravity<\/a>, Vol. 19, pp. 1709-1716, 2002. [pdf<\/a>, 152K] <\/li>\n<\/ol>\n\n\n\n
    Peer-reviewed full-length conference articles<\/strong><\/h5>\n\n\n\n
      \n
    1. Few-Shot Image Classification Along Sparse Graphs<\/em>. Joseph F. Comer, Philip L. Jacobson, and Heiko Hoffmann<\/strong>. <\/a>CVPR Workshop on Learning with Limited Labelled Data for Image and Video Understanding<\/a>, 2022. [pdf<\/a>, 848K] <\/li>\n\n\n\n
    2. Pooling by Sliced-Wasserstein Embedding<\/em>. Navid Naderializadeh, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann<\/strong>, and Soheil Kolouri. Conference on Neural Information Processing Systems (NeurIPS)<\/a>, 2021. [pdf<\/a>, 992K] <\/li>\n\n\n\n
    3. Wasserstein Embedding for Graph Learning<\/em>. Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, and Heiko Hoffmann<\/strong>. <\/a>International Conference on Learning Representations (ICLR)<\/a>, 2021. [pdf<\/a>, 1.8M] <\/li>\n\n\n\n
    4. Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs<\/em>. Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, and Heiko Hoffmann<\/strong>. <\/a>Conference on Computer Vision and Pattern Recognition (CVPR)<\/a>, 2020. (oral presentation)[pdf<\/a>, 2.6M] <\/li>\n\n\n\n
    5. Explainability Methods for Graph Convolutional Neural Networks<\/em>. Phillip Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann<\/strong>. <\/a>Conference on Computer Vision and Pattern Recognition (CVPR)<\/a>, 2019. (oral presentation, acceptance rate for orals: 5%) [pdf<\/a>, 2.2M] <\/li>\n\n\n\n
    6. Sliced Wasserstein Distance for Learning Gaussian Mixture Models<\/em>. Soheil Kolouri, Gustavo K. Rohde, and Heiko Hoffmann<\/strong>. <\/a>Conference on Computer Vision and Pattern Recognition (CVPR)<\/a>, 2018. [pdf<\/a>, 4.1M] <\/li>\n\n\n\n
    7. Explaining Distributed Neural Activations via Unsupervised Learning<\/em>. Soheil Kolouri, Charles E Martin, and Heiko Hoffmann<\/strong>. <\/a>Conference on Computer Vision and Pattern Recognition<\/a>, Explainable Computer Vision Workshop, 2017. (oral presentation) [pdf<\/a>, 1.6M] <\/li>\n\n\n\n
    8. Fast Re-learning of a Controller from Sparse Data<\/em>. Charles E Martin and Heiko Hoffmann. <\/strong>IEEE International Conference on Systems, Man, and Cybernetics<\/a>, 2014. (oral presentation) [pdf<\/a>, 308K] <\/li>\n\n\n\n
    9. Fast pattern matching with time-delay neural networks<\/em>. Heiko Hoffmann<\/strong>, Michael Howard, and Michael Daily. International Joint Conference on Neural Networks<\/a>, 2011. (oral presentation) [<\/a>pdf<\/a>, 1.3M] <\/li>\n\n\n\n
    10. Toward Ultra High Speed Locomotors: Design and Test of a Cheetah Robot Hindlimb<\/em>. M. Anthony Lewis, Matthew R Bunting, Behnam Salemi, and Heiko Hoffmann<\/strong>. IEEE International Conference on Robotics and Automation<\/a>, 2011. (oral presentation) [<\/a>pdf<\/a>, 1.5M] <\/li>\n\n\n\n
    11. Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance<\/em>. Heiko Hoffmann<\/strong>, Peter Pastor, Dae-Hyung Park, and Stefan Schaal. IEEE International Conference on Robotics and Automation<\/a>, 2009. (oral presentation) [<\/a>pdf<\/a>, 692K] <\/li>\n\n\n\n
    12. Learning and Generalization of Motor Skills by Learning from Demonstration<\/em>. Peter Pastor, Heiko Hoffmann<\/strong>, Tamim Asfour, and Stefan Schaal. IEEE International Conference on Robotics and Automation<\/a>, 2009. (oral presentation) [pdf<\/a>, 1548K] <\/li>\n\n\n\n
    13. Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields<\/em>. Dae-Hyung Park, Heiko Hoffmann<\/strong>, Peter Pastor, and Stefan Schaal. IEEE International Conference on Humanoid Robots<\/a>, 2008. (oral presentation) [<\/a>pdf<\/a>, 672K] <\/li>\n\n\n\n
    14. Sensor-assisted adaptive motor control under continuously varying context<\/em>. Heiko Hoffmann<\/strong>, Georgios Petkos, Sebastian Bitzer, and Sethu Vijayakumar. Proc. International Conference on Informatics in Control, Automation, and Robotics, presented at ICINCO<\/a>, 2007. (oral presentation) [pdf<\/a>, 1096K] <\/li>\n\n\n\n
    15. Action Selection and Mental Transformation Based on a Chain of Forward Models<\/em>. Heiko Hoffmann<\/strong>, Ralf M\u00f6ller. Proceedings of the 8th Conference on Simulation of Adaptive Behavior (SAB ’04), pp. 213-222, S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam, and J.-A. Meyer (Eds.), MIT Press, 2004. (oral presentation) [pdf<\/a>, 612K] <\/li>\n\n\n\n
    16. Learning internal models for eye-hand coordination in reaching and grasping<\/em>. Wolfram Schenck, Heiko Hoffmann<\/strong>, Ralf M\u00f6ller. Proc. EuroCogSci, pp. 289-294, F. Schmalhofer, R. M. Young, G. Katz (Eds.), Lawrence Erlbaum Associates: Mahwah, New Jersey, 2003. (oral presentation) [pdf<\/a>, 872K] <\/li>\n\n\n\n
    17. Unsupervised Learning of a Kinematic Arm Model<\/em>. Heiko Hoffmann<\/strong>, Ralf M\u00f6ller. Artificial Neural Networks and Neural Information Processing – ICANN\/ICONIP, O. Kaynak, E. Alpaydin, E. Oja, L. Xu (Eds.), 2003. (oral presentation), LNCS<\/a> 2714, pp. 463-470, Springer, Heidelberg [pdf<\/a>, 158K] <\/li>\n<\/ol>\n\n\n\n
      Peer-reviewed short conference articles<\/strong><\/h5>\n\n\n\n
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      1. Interactive Audio-Tactile Annotation of 3D Point Clouds for Robotic Manipulation<\/em>. Jivko Sinapov, Darren Earl, Derek Mitchell, and Heiko Hoffmann<\/strong>. ICRA Mobile Manipulation Workshop on Interactive Perception, 2013 [pdf<\/a>, 370K]<\/li>\n\n\n\n
      2. Simple finger movements require complex coordination of excursions and forces across all muscles<\/em>. Jason Kutch, Manish Kurse, Heiko Hoffmann<\/strong>, Evangelos Theodorou, Rod Hentz, Caroline Leclercq, Isabella Fassola, and Francisco Valero-Cuevas. Annual Meeting for the American Society of Biomechanics, State College, Pennsylvania, August 26-29, 2009<\/li>\n\n\n\n
      3. Optimization strategies in human reinforcement learning<\/em>. Heiko Hoffmann<\/strong>, Evangelos Theodorou, and Stefan Schaal. Advances in Computational Motor Control VII<\/a>, Symposium at the Society for Neuroscience Meeting, Washington DC, 2008 [pdf<\/a>, 104K]<\/li>\n\n\n\n
      4. Dynamic movement primitives for movement generation motivated by convergent force fields in frog<\/em>. Heiko Hoffmann<\/strong>, Peter Pastor, and Stefan Schaal. Adaptive Motion of Animals and Machines<\/a> (AMAM), Cleveland, Ohio, 2008[pdf<\/a>, 212K]<\/li>\n\n\n\n
      5. Movement generation by learning from demonstration and generalization to new targets<\/em>. Peter Pastor, Heiko Hoffmann<\/strong>, and Stefan Schaal. Adaptive Motion of Animals and Machines<\/a> (AMAM), Cleveland, Ohio, 2008 [pdf<\/a>, 204K]<\/li>\n\n\n\n
      6. Combining dynamic movement primitives and potential fields for online obstacle avoidance<\/em>. Dae-Hyung Park, Heiko Hoffmann<\/strong>, and Stefan Schaal. Adaptive Motion of Animals and Machines<\/a> (AMAM), Cleveland, Ohio, 2008 [pdf<\/a>, 240K]<\/li>\n\n\n\n
      7. Human movement generation based on convergent flow fields: A computational model and a behavioral experiment<\/em>. Heiko Hoffmann<\/strong> and Stefan Schaal. Advances in Computational Motor Control VI<\/a>, Symposium at the Society for Neuroscience Meeting, San Diego, 2007[pdf<\/a>, 104K]<\/li>\n\n\n\n
      8. Modellierung der Rissdynamik in tonigen B\u00f6den<\/em>Andreas Leopold, Heiko Hoffmann<\/strong>, Hans-J\u00f6rg Vogel, Kurt Roth. Mitt. Dtsch. Bodenkundl. Ges., Vol. 102<\/a>, pp. 109-110, 2003<\/li>\n<\/ol>\n\n\n\n
        Abstracts<\/strong><\/h5>\n\n\n\n
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        1. Are reaching movements planned in kinematic or dynamic coordinates?<\/em> Alice Ellmer, Heiko Hoffmann<\/strong>, and Stefan Schaal. Neural Control of Movement<\/a>, Naples, Florida, 2010 [html<\/a>] [poster<\/a>]<\/li>\n\n\n\n
        2. Error-feedback control through a forward model predicts preferential control of task-relevant parameters as observed in human finger muscle activation<\/em>. Heiko Hoffmann<\/strong>, Evangelos Theodorou, and Francisco Valero-Cuevas. Society for Neuroscience<\/a>, Chicago, Il, 2009<\/li>\n\n\n\n
        3. Muscle synergies may be artifacts of biomechanics rather than neural constraints, and are not necessary to simplify control<\/em>. Jason Kutch, Manish Kurse, Heiko Hoffmann<\/strong>, Art Kuo, and Francisco Valero-Cuevas. Society for Neuroscience<\/a>, Chicago, Il, 2009<\/li>\n\n\n\n
        4. Control of muscle strain energy as a robust means to produce slow and accurate finger movements: Proof of concept via hardware and cadaver implementation<\/em>. Heiko Hoffmann<\/strong>, Jason Kutch, Manish Kurse, and Francisco Valero-Cuevas. Neural Control of Movement<\/a>, Waikoloa, Hawaii, 2009 [html<\/a>] [poster<\/a>]<\/li>\n\n\n\n
        5. Human optimization strategies under reward feedback<\/em>. Heiko Hoffmann<\/strong>, Evangelos Theodorou, and Stefan Schaal. Neural Control of Movement<\/a>, Waikoloa, Hawaii, 2009 [html<\/a>] [poster<\/a>]<\/li>\n\n\n\n
        6. Do humans plan continuous trajectories in kinematic coordinates?<\/em> Heiko Hoffmann<\/strong> and Stefan Schaal. Society for Neuroscience<\/a>, Washington, D.C., 2008 [html<\/a>] [poster<\/a>]<\/li>\n\n\n\n
        7. Behavioral experiments on reinforcement learning in human motor control<\/em>. Heiko Hoffmann<\/strong>, Evangelos Theodorou, and Stefan Schaal. Neural Control of Movement<\/a>, Naples, Florida, 2008 [html<\/a>]<\/li>\n\n\n\n
        8. The dual role of uncertainty in force field learning<\/em>. Michael Mistry, Evangelos Theodorou, Heiko Hoffmann<\/strong>, and Stefan Schaal. Neural Control of Movement<\/a>, Naples, Florida, 2008 [html<\/a>]<\/li>\n\n\n\n
        9. A computational model of human trajectory planning based on convergent flow fields<\/em>. Heiko Hoffmann<\/strong> and Stefan Schaal. Society for Neuroscience<\/a>, San Diego, California, 2007 [html<\/a>]<\/li>\n\n\n\n
        10. A computational model of arm trajectory modification using dynamic movement primitives<\/em>. Peyman Mohajerian, Heiko Hoffmann<\/strong>, Michael Mistry, and Stefan Schaal. Society for Neuroscience<\/a>, San Diego, California, 2007<\/li>\n\n\n\n
        11. Uncertain 3D force fields in reaching movements: Do humans favor robust or average performance?<\/em> Michael Mistry, Evangelos Theodorou, Heiko Hoffmann<\/strong>, and Stefan Schaal. Society for Neuroscience<\/a>, San Diego, California, 2007 <\/li>\n<\/ol>\n\n\n\n
          Invited articles<\/strong><\/h5>\n\n\n\n
            \n
          1. SAR automatic target recognition with less labels<\/em>. Joseph F. Comer, Reed W. Andrews, Navid Naderializadeh, Soheil Kolouri, and Heiko Hoffmann<\/strong>. Proc. SPIE 11394, Automatic Target Recognition XXX, 113940Q, 24 April 2020<\/li>\n\n\n\n
          2. Rapid 3D registration using local subtree caching in Iterative Closest Point (ICP) algorithm<\/em>. Ryan Uhlenbrock, Kyungnam Kim, Heiko Hoffmann<\/strong>, and Jean J. Dolne. SPIE Optics and Photonics<\/a>, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing, 2017<\/li>\n\n\n\n
          3. Classification and segmentation of orbital space-based objects against terrestrial distractors for the purpose of finding holes in Shape from Motion 3D reconstruction<\/em>. T. Nathan Mundhenk, Arturo Flores, and Heiko Hoffmann<\/strong>. SPIE Electronic Imaging<\/a>, 2014 [pdf<\/a>, 8.9M]<\/li>\n\n\n\n
          4. Biologically-inspired image processing for machine grasping<\/em>. Heiko Hoffmann<\/strong>. The Neuromorphic Engineer, Vol. 2, Issue 2, 2005, p. 7 [pdf<\/a>, 2.1M] <\/li>\n<\/ol>\n\n\n\n
            Letters<\/strong><\/h5>\n\n\n\n
              \n
            1. Failure-based resource allocation is insufficient to prevent future catastrophic blackouts<\/em>. Heiko Hoffmann<\/strong> and David W. Payton. Science<\/a>, eLetter in response to Small vulnerable sets determine large network cascades in power grids, Vol. 358, Issue 6365, eaan3184, 2017. Feb 1, 2018 <\/li>\n<\/ol>\n\n\n\n
              Books<\/strong><\/h5>\n\n\n\n
                \n
              1. Unsupervised Learning of Visuomotor Associations<\/em>. Heiko Hoffmann<\/strong>. MPI Series in Biological Cybernetics<\/a>, Vol. 11, 2005, Logos Verlag Berlin<\/a>, ISBN 3-8325-0858-9, PhD thesis, Faculty of Technology, Bielefeld University [pdf<\/a>, 7800KB] [html<\/a>] <\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

                Google Scholar Citations Peer-reviewed journal articles Peer-reviewed full-length conference articles Peer-reviewed short conference articles Abstracts Invited articles Letters Books<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-18","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/pages\/18"}],"collection":[{"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/comments?post=18"}],"version-history":[{"count":29,"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/pages\/18\/revisions"}],"predecessor-version":[{"id":516,"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/pages\/18\/revisions\/516"}],"wp:attachment":[{"href":"https:\/\/heikohoffmann.de\/index.php\/wp-json\/wp\/v2\/media?parent=18"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}