Publications

 

 

Jun Tani’s lab Publication Lists (Updated Dec 16, 2016)  Citation Index

 

Tani, J. (2016). Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena. New York: Oxford University Press.

Cover image of book "Exploring Robotic Minds", by Jun Tani

Selected list of publications

  1. Tani, J. (2017). Exploring Robotic Minds by Predictive Coding Principle. The Newsletter of the Technical Committee on Cognitive and Developmental Systems. 14(1) , 4-5. Spring. PDF
    • This dialog is initiated by Tani's writing about predictive coding used for robots based on recent Tani's book, followed by commentaries made by interesting researchers such as Andy Clark, Karl Friston, Pezzulo and others, and replies by Tani for them in the end.
  2. Choi, M., & Tani, J. (2017). Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model. accepted in Neural Computation. PDF
  3. Ahmadi A., & Tani, J. (2017). Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model. arXiv:1706.10240, 2017. PDF
    • The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.
  4. Tani, J. (2014). Self-Organization and Compositionality in Cognitive Brains: A Neuro-Robotics Study. Proceedings of the IEEE, Special Issue on Cognitive Dynamic Systems, 102(4), 586-605. PDF
  5. Namikawa, J., Nishimoto, R., & Tani, J.(2011). A neurodynamic account of spontaneous behaviour”, PLoS Computational Biology, Vol. 7, Issue 10, e1002221. PDF
    • This paper suggests that deterministic chaos self-organized in cortical dynamics could be responsible for the generation of spontaneous action sequences. We conducted experiments of tutoring a humanoid robot for stochastic transition sequences of primitive actions by utilizing multiple timescales recurrent neural network model (MTRNN). The experimental results showed that the robot successfully learned to imitate tutored behavioral sequence patterns by extracting the underlying transition probability among primitive actions. An analysis revealed that a set of primitive action patterns was embedded in the fast dynamics part and the chaotic dynamics of spontaneously sequencing these action primitive patterns was structured in the slow dynamics part, provided that the timescale was adequately set for each part. By making analogy of the model to the known cortical hierarchy for action generation, it is speculated that the slow dynamics part of self-organizing chaos might correspond to the prefrontal cortex that is known for its flexible production of actions. The study also sheds a ray of light on the complementary relation between learning of stochastic processes and that of deterministic ones.
  6. Yamashita, Y., & Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Computational Biology, Vol.4, Issue.11, e1000220 PDF
    • This paper examines how functional hierarchy can self-organize through sensory-motor interactions, without assuming predefined level-structured functions. A humanoid robot was implemented with so-called the multipe timescales recurrent neural network (MTRNN). The MTRNN consists of the fast neurons part and the slow neurons one which are interconnected each other within a single network. The results of the robot learning experiments showed that functional hierarchy emerges with accompanying a compositional structure such that the continuous sensory-motor flow is segmented into reusable behavior primitives in the fast neurons part and those primitives are integrated into specified goal-directed actions in the slow neuron part.
  7. Sugita, Y., & Tani, J. (2005). Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adaptive Behavior, 13(1), 33-52. PDF
    • This paper shows how meaning space can be self-organized through dynamic interactions between a linguistic module and a behavior module which are implemented by RNNPB models. The robotic learning experiment showed that compositional structure represented by combinations of verbs and objective nouns appear as generalized with forming distributed representations in the network.
  8. Tani, J., Ito, M., & Sugita, Y. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB. Neural Networks, 17, 1273-1289. PDF
    • This paper describes a mirror neuron model from dynamical systems perspective. Forward predictive models (generative models) are learned for multiple goal-directed actions distributedly in a single RNN. In action generation, units called parametric bias (PB) play role of bifurcation parameter for the RNN dynamics to generate multiple goal-directed actions. On the other hand in recognizing actions, the best PB values to fit with perceived sensory sequences are inferred through inverse computation. A set of robotics experiments evaluate how generalization by learning can be achieved with this distributed representation scheme.
  9. Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Proc. 5th Int. Conf. on Simulation of Adaptive Behavior, (Eds) R. Pfeifer, B. Blumberg, J.A. Meyer and S.W. Wilson, MA: The MIT Press, pp.270-279. The revised version is in Neural Networks, 12, 1131-1141. PDF
    • This paper shows how continuous sensory-motor flow can be segmented into hierarchically organized chunks through anticipatory learning of local mixture of RNN experts with multiple levels. The study addresses the issue of how compositional representation can emerge solely through row sensory-motor experiences using a localist neural network model.
  10. Tani, J. (1998). An interpretation of the ‘Self’ from the dynamical systems perspective: a constructivist approach. Journal of Consciousness Studies, 5(5/6), 516-542. PDF
    • This study attempts to describe the notion of the "self" from dynamical systems perspective based on our robot experiments. A vision-based mobile robot implemented with an RNN model learns to predict landmark sequences experienced during its dynamic exploration of environment. It was shown that the learning process switches spontaneously between coherent phases in which the top-down prediction agrees with the bottom-up sensation and incoherent phases in which conflicts appear between the two. By investigating possible analogies between this result and the phenomenological literature on the "self", we draw the conclusions that (1) the structure of the "self" corresponds to the "open dynamic structure" which is characterized by co-existence of stability in terms of goal-directedness and instability caused by embodiment; (2) the open dynamic structure causes the system's spontaneous transition to the unsteady phase where the "self" becomes aware.
  11. Tani, J. (1996). Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans. on Syst. Man and Cybern. Part B-Cybernetics, 26(3), 421-436. PDF
    • This paper describes a neuro-robotics experiment to show how 'symbolic structure' emerges as embedded in neuronal dynamics as the results of internalizing experiences of combinatorial sensory-motor interactions of robots. The action-sensation causality is learned as a forward model by using a Jordan-type recurrent neural net (RNN) which is implemented in a mobile robot. After the learning, the RNN generated on-line prediction of next sensation for given action as well as mental simulations for combinatorial action sequences without actual movements. Our dynamical system analysis showed that a finite state machine like symbolic structure emerges in a fractal-like global attractor of the RNN dynamics which is naturally situated with sensory-motor interactions with environment.
  12. Tani, J., & Fukumura, N. (1995). Embedding a grammatical description in deterministic chaos: an experiment in recurrent neural learning. Biological Cybernetics, 72, 365-370. PDF
    • This paper describes how symbolic dynamics can be learned in RNN. 
      A Jordan type RNN was trained with stochastic symbolic sequences with a grammar. The learning result showed that the stochastic symbolic sequences are reconstructed by self-organizing deterministic chaos in RNN.

Journals

  1. White, J., & Tani, J. (2017). From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3). American Philosophy Association Newsletter, Philosophy and Computers, 17(1), 11-22. PDF
  2. Lee, H., Jung, M., & Tani, J. (2017). Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks. IEEE Transactions on Cognitive and Developmental Systems. accepted. PDF
  3. Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2017). Lifelong learning of human actions with deep neural network self-organization. Neural Networks. 96 (2017), 137–149. PDF
  4. Tatsch, C., Ahmadi, A., Bottega. F., Tani, J., da Silva Guerra, R. (2017). Dimitri: An Open-Source Humanoid Robot with Compliant Joints. Journal of Intelligent & Robotic Systems. in press
  5. Tani, J. (2017). Exploring Robotic Minds by Predictive Coding Principle. The Newsletter of the Technical Committee on Cognitive and Developmental Systems. 14(1) , 4-5. Spring. PDF
  6. Choi, M., & Tani, J. (2017). Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model. accepted in Neural Computation. PDF
  7. Hwang, J., & Tani, J. (2017). Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach. Accepted in IEEE Trans. on Cognitive and Developmental Systems. arXiv preprint arXiv:1706.02423, DOI: 10.1109/TCDS.2017.2714170
  8. Tani, J., & White, J. (2017). From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 2). American Philosophy Association Newsletter, Philosophy and Computers, 16(2), 29-41. PDF
  9. Ahmadi, A., & Tani, J. (2017). How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction. Neural Networks. 92, 3-16, DOI:10.1016/j.neunet.2017.02.015 PDF.
  10. Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2017). Emergence of multimodal action representations from neural network self-organization. Cognitive Systems Research, 43, 208-221.
  11. Lyon, C. et al. (2016). Embodied language learning and cognitive bootstrapping: Methods and design principles. International Journal of Advanced Robotics Systems, 13:105, DOI:10.5772/63462
  12. White, J., & Tani, J. (2016). From biological to synthetic neurorobotics approaches to understanding the structure essential to consciousness. (Part 1). American Philosopher Association Newsletter, Philosophy and Computers, 16(2), 13-23. PDF
  13. Murata, S., Yamashita, Y., Arie, H., Ogata, T., Sugano, S., & Tani, J. (2015). Learning to perceive the world as probabilistic or deterministic via interaction with others: a neuro-robotics experiment. IEEE Transactions on Neural Networks and Learning Systems, (4), 830-848. DOI: 10.1109/TNNLS.2015.2492140 PDF
  14. Park, G., & Tani, J. (2015). Development of compositional and contextual communicable congruence in robots by using dynamic neural network models. Neural Networks, 72, 109-122. PDF
  15. Jung, M., Hwang, J., & Tani, J. (2015). Self-organization of spatio-temporal hierarchy via learning of dynamic visual image patterns on action sequences. PLoS One, 10(7): e0131214, DOI:10.1371/journal.pone.0131214 PDF
  16. Tani, J. (2014). Self-Organization and Compositionality in Cognitive Brains: A Neuro-Robotics Study. Proceedings of the IEEE, Special Issue on Cognitive Dynamic Systems, 102(4), 586-605. PDF
  17. Tani, J., Friston, K., & Haykin, S. (2014). Further Thoughts on the paper by Tani: Self-Organization and Compositionality in Cognitive Brains. Proceedings of the IEEE, Special Issue on Cognitive Dynamic Systems, 102(4), 606-607. PDF
  18. Murata, S., Arie, H., Ogata, T., Sugano, S., & Tani, J. (2014). Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism. Advanced Robotics, 28(17), 1189-1203, DOI: 10.1080/01691864.2014.916628
  19. Komatsu, M., Namikawa, J., Chao, Z. C., Nagasaka, Y., Fujii, N., Nakamura, K., & Tani, J. (2014). An artificial network model for estimating the network structure underlying partially observed neuronal signals. Neuroscience Research, 81-82, 69-77, DOI: 10.1016/j.neures.2014.02.005
  20. Murata, S., Namikawa, J., Arie, H., Sugano, S., & Tani, J. (2013). Learning to reproduce fluctuating time series by inferring their time-dependent stochastic properties: Application in robot learning via tutoring. IEEE Transactions on Autonomous Mental Development, 5(4), 298-310, DOI: 10.1109/TAMD.2013.2258019 PDF
  21. Jeong, S., Park, Y., Mallipeddia, P., Tani, J., & Lee, M. (2013). Goal-oriented Behavior Sequence Generation based on Semantic Commands using Multiple Timescales Recurrent Neural Network with Initial State Correction. Neurocomputing, 129, 67-77
  22. Alnajjar, F., Yamashita, Y., & Tani, J. (2013). The Hierarchical and Functional Connectivity of Higher-order Cognitive Mechanisms: Neurorobotic Model to Investigate the Stability and Flexibility of Working Memory. Frontiers in Neurorobotics, Vol. 7, Article 2, February. PDF
  23. Yamashita, Y., & Tani, J. (2012). Spontaneous Prediction Error Generation in Schizophrenia.  PLoS One, 7(5): e37843. doi:10.1371/journal.pone.0037843 PDF
  24. Maniadakisa, M., Trahaniasa, P., & Tani, J. (2012). Self-organizing high-order cognitive functions in artificial agents: implications for possible prefrontal cortex mechanisms. Neural Networks, 33, 76-87. PDF
  25. Nishide, S., Tani, J., Takahashi, T., Okuno, H.G., & Ogata, T. (2012) Tool-Body assimilation of humanoid robot using neuro-dynamical system. IEEE Trans. on Autonomous Mental Development, 14, 139-149.
  26. Arie, H., Arakaki, T., Sugano, S., & Tani, J. (2011). Imitating others by composition of primitive actions: a neuro-dynamic model. Robotics and Autonomous Systems, 60, 729-741. PDF
  27. Tobari, Y., Okumura, T., Tani, J., & Okanoya, K. (2011). A direct neuronal connection between the telencephalic nucleus robustus arcopallialis and the nucleus nervi hypoglossi, pars tracheosyringealis in Bengalese finches (Lonchura striata var. domestica). Neuroscience Research, 71(4), 361-368.
  28. Namikawa, J., Nishimoto, R., & Tani, J.(2011). A neurodynamic account of spontaneous behaviour”, PLoS Computational Biology, Vol. 7, Issue 10, e1002221. PDF
  29. Rohlfing, K.J., &Tani, J. (2011). Grounding language in action. IEEE Transactions on Autonomous Mental Development, 3(2), 109-112. PDF
  30. Jeong, S., Arie, H., Lee, M., & Tani, J. (2011). Neuro-robotics study on integrative learning of proactive visual attention and motor behaviors. Cognitive Neurodynamics, 6, 43-59. PDF
  31. Sugita, Y., Tani, J., & Butz, M.V. (2011). Simultaneously emerging braitenberg codes and compositionality. Adaptive Behavior, 19(5), 295-316. PDF
  32. Yamashita, Y., Okumura, T., Okanoya, K., & Tani, J. (2011). Cooperation of deterministic dynamics and random noise in production of complex syntactical avian song sequences: a neural network model. Frontiers in Computational Neuroscience, 5(18), 1-12. PDF
  33. Nishide, S., Tani, J., Okuno, H.G. & Ogata, T. (2011). Towards written text recognition based on handwriting experiences using recurrent neural network. Advanced Robotics, 25(17), 2173-2187.
  34. Hinoshita, W., Arie, H., Tani, J., Okuno, H. & Ogata, T. (2011). Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network. Neural Networks, 24, 311-320. PDF
  35. Cangelosi, A., Metta, G., Sagerer, G., Nolfi, S., Nehaniv, C.L., Fischer, K., Tani, J., Belpaeme, B., Sandini, G., Fadiga, L., Wrede, B., Rohlfing, K., Tuci, E., Dautenhahn, K., Saunders, J. & Zeschel, A. (2010). Integration of action and language knowledge: A roadmap for developmental robotics. IEEE Transactions on Autonomous Mental Development, 2(3), 167-195. PDF
  36. Tani, J. (2010). Studies of symbols from ‘Robot Science’. Journal of the Robotics Society of Japan, 28(4), 522-531. PDF (in Japanese)
  37. Namikawa, J. & Tani, J. (2010). Learning to imitate stochastic time series in a compositional way by chaos. Neural Networks, 23, 625-638. PDF
  38. Maniadakis, M., Trahanias, P., & Tani, J. (2009). Explorations on artificial time perception. Neural Networks, 22, 509-517. PDF
  39. Tani, J. (2009). Autonomy of ‘Self’ at criticality: The perspective from synthetic neuro-robotics. Adaptive Behavior, 17(5), 421-443. PDF
  40. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2009). Self-organization of dynamic object features based on bidirectional training. Advanced Robotics, 23, 2035-2057.
  41. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2009). Autonomous motion generation based on reliable predictability. Journal of Robotics and Mechatronics, 21(4), 478-488.
  42. Nishimoto, R., & Tani, J. (2009). Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study. Psychological Research, 73, 545-558. PDF
  43. Arie, H., Endo, T., Arakaki, T., Sugano, S., & Tani, J. (2009). Creating novel goal-directed actions at criticality: a neuro-robotic experiment. New Mathematics and Natural Computation, 5(1), 307-334. PDF
  44. Maniadakis, M., & Tani, J. (2009). Acquiring rules for rules: neuro-dynamical systems account for meta-cognition. Adaptive Behavior, 17(1), 58-80. PDF
  45. Igari, I., & Tani, J. (2009). Incremental learning of sequence patterns with a modular network model. Neurocomputing, 72, 1910-1919. PDF
  46. Tani, J. (2008). Objectifying the subjective self: An account from a synthetic robotics approach. Constructivist Foundations, 4(1), 28-30. PDF
  47. Namikawa, J., & Tani, J. (2008). Building recurrent neural networks to implement multiple attractor dynamics using the gradient descent method. Advances in Artificial Neural Systems, Vol. 2009, Article ID 846040. PDF
  48. Yamashita, Y., & Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Computational Biology, Vol.4, Issue.11, e1000220. PDF
  49. Namikawa, J., & Tani, J. (2008). A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance. Neural Networks, 21, 1466-1475. PDF
  50. Yamashita, Y., Takahashi, M., Okumura, T., Ikebuchi, M., Yamada, H., Suzuki, M., Okanoya, K., & Tani, J. (2008). Developmental learning of complex syntactical song in theBengalese finch: A neural network model. Neural Networks, 21, 1224-1231. PDF
  51. Tani, J., Nishimoto, R., & Paine, R.W. (2008). Achieving ‘organic compositionality’ through self-organization: Reviews on brain-inspired robotics experiments. Neural Networks, 21, 584-603. PDF
  52. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2008). Predicting object dynamics from visual images through active sensing experiences. Advanced Robotics, 22(5), 527-546. PDF
  53. Nishimoto, R., Namikawa, J., & Tani, J. (2008). Learning multiple goal-directed actions through self-organization of a dynamic neural network model: a humanoid robot experiment. Adaptive Behavior, 16(2/3), 166-181. PDF
  54. Tani, J., Nishimoto, R., Namikawa, J., & Ito, M. (2008). Codevelopmental learning between human and humanoid robot using a dynamic neural-network model. IEEE Trans. on Syst. Man and Cybern. Part B-Cybernetics, 38(1), 43-59. PDF
  55. Tani, J. (2007). On the interactions between top-down anticipation and bottom-up regression. Frontiers in Neurorobotics, Vol. 1, Article 2. PDF
  56. Okumura, T., Okanoya, K., & Tani, J. (2007). Application of light-cured dental adhesive resin for mounting electrodes or microdialysis probes in chronic experiments. Journal of Visualized Experiments, 6, 249-1~249-10.
  57. Yokoya, R., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2007). Experience-based imitation using RNNPB. Advanced Robotics, 21(12), 1351-1367. PDF
  58. Arie, H., Ogata, T., Tani, J., & Sugano, S. (2007). Reinforcement learning of a continuous motor sequence with hidden states. Advanced Robotics, Special Issue on Robotic Platforms for Research in Neuroscience, 21(10), 1215-1229. PDF
  59. Ito, M., Noda, K., Hoshino, Y., & Tani, J. (2006). Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks, 19, 323-337. PDF
  60. Tobari, Y., Okumura, T., Tani, T., & Okanoya, K. (2006). Non-singing female Bengalese Finches (Lonchura striata var. domestica) possess neuronal projections connecting a song learning region to a song motor region. Ornithological Science, 5, 47-55.
  61. Ogata, T., Ohba, H., Tani, J., Komatani, K., & Okuno, H.G. (2005). Extracting multimodal dynamics of objects using RNNPB. Journal of Robotics and Mechatronics, 17(6), 681-688.
  62. Ogata, T., Sugano, S., & Tani, J. (2005). Open-end human-robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning. Advanced Robotics, 19(6), 651-670.
  63. Ogata, T., Sugano, S., & Tani, J. (2005). Acquisition of motion primitives of robot in human-navigation task. Journal of Japanese Society for Artificial Intelligence, 20(3), 188-196. PDF
  64. Paine, R.W., & Tani, J. (2005). How hierarchical control self-organizes in artificial adaptive systems. Adaptive Behavior, 13(3), 211-225. PDF
  65. Sugita, Y., & Tani, J. (2005). Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adaptive Behavior, 13(1), 33-52. PDF
  66. Ito, M., & Tani, J. (2004). On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system. Adaptive Behavior, 12(2), 93-115. PDF
  67. Tani, J. (2004). The dynamical systems accounts for phenomenology of immanent time: an interpretation by revisiting a robotics synthetic study. Journal of Consciousness Studies, 11(9), 5-24. PDF
  68. Paine, R.W., & Tani, J. (2004). Motor primitive and sequence self-organization in a hierarchical recurrent neural network. Neural Networks, 17, 1291-1309. PDF
  69. Tani, J., Ito, M., & Sugita, Y. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB. Neural Networks, 17, 1273-1289. PDF
  70. Nishimoto, R., & Tani, J. (2004). Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems. Neural Networks, 17, 925-933. PDF
  71. Tan,i J., & Ito, M. (2003). Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment. IEEE Trans. on Syst. Man and Cybern. Part A- Systems and Humans, 33(4), 481-488. PDF
  72. Tani, J. (2003). Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Networks, 16(1), 11-23. ps.Z PDF
  73. Tani, J., & Yamamoto, J. (2002). On the dynamics of robot exploration learning. Cognitive Systems Research, 3(3), 459-470. ps.Z PDF
  74. Nolfi, S., & Tani, J. (1999). Extracting regularities in space and time through a cascade of prediction of prediction networks: The case of a mobile robot navigating in a structured environment. Connection Science, 11(2), 125-148.
  75. Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Proc. 5th Int. Conf. on Simulation of Adaptive Behavior, (Eds) R. Pfeifer, B. Blumberg, J.A. Meyer, S.W. Wilson, MA: The MIT Press, pp.270-279. The revised version is in Neural Networks, 12, 1131-1141. ps.Z PDF
  76. Tani, J. (1998). An interpretation of the ‘Self’ from the dynamical systems perspective: a constructivist approach. Journal of Consciousness Studies, 5(5/6), 516-542. ps.Z PDF
  77. Tani, J., & Nolfi, S. (1997). Self-organization of modules and their hierarchy in robot learning problems: A dynamical systems approach. System Analysis for Higher Brain Function Research Project News Letter, 2(4), 1-11. PDF
  78. Tani, J., & Fukumura, N. (1997). Self-organizing internal representation in learning of navigation: a physical experiment by the mobile robot YAMABICO. Neural Networks, 10(1), 153-159. PDF
  79. Tani, J. (1996). Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans. on Syst. Man and Cybern. Part B-Cybernetics, 26(3), 421-436. PDF
  80. Tani, J., & Fukumura, N. (1995). Embedding a grammatical description in deterministic chaos: an experiment in recurrent neural learning. Biological Cybernetics, 72, 365-370. ps.Z  PDF
  81. Fukumura, N., & Tani, J. (1994). Learning in robotics. Learning goal-directed behaviour as dynamical system for sensory motor system. Journal of the Robotics Society of Japan, 13(1), 75-81.
  82. Tani, J., & Fukumura, N. (1994). Learning goal-directed sensory-based navigation of a mobile robot. Neural Networks, 7(3), 553-563.
  83. Tani, J. (1992). Proposal of chaotic steepest descent method for neural networks and analysis of their dynamics. Electronics and Communications in Japan, Part 3, 75(4), 62-70.
  84. Tani, J., & Fujita, M. (1992). Coupling of memory search and mental rotation by a nonequilibrium dynamics neural network. IEICE Trans. Fundamentals, E75-A(5), 578-585.

International Conference Proceedings, Book Chapter, Review Paper

  1. Ahmadi, A., & Tani, J. (2017). Bridging the Gap between Probabilistic and Deterministic Models: A imulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model. arXiv:1706.10240. PDF
  2. Hwang, J., Kim, J., Ahmadi, A., Choi, M., & Tani, J. (2017). Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning. Accepted at the 7th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics (ICDL-EpiRob 2017). PDF
  3. Ahamdi, A., & Tani, J. (2016). Towards Robustness to Fluctuated Perceptual Patterns by a Deterministic Predictive Coding Model in a Task of Imitative Synchronization with Human Movement Patterns. Proc. of International Conference on Neural Information Processing, October, 393-402. PDF (got Excellent Paper Award)
  4. Hwang, J., Jung, M., & Tani, J. (2016). A Deep Learning Approach for Seamless Integration of Cognitive Skills for Humanoid Robots. Proc. of International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) 2016. PDF
  5. Chen, Y., Murata, S., Arie, H., Ogata, T., Tani, J., & Sugano, S. (2016). Emergence of Interactive Behaviors between Two Robots by Prediction Error Minimization Mechanism. Proc. of International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) 2016. PDF
  6. Choi, M., & Tani, J. (2016). Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model. arXiv:1606.01672v2. PDF
  7. Lee, H., Jung, M., & Tani, J., (2016). Characteristics of Visual Categorization of Long-Concatenated and Object-Directed Human Actions by a Multiple Spatio-Temporal Scales Recurrent Neural Network Model. arXiv:1602.01921v1. PDF
  8. Park, G., & Tani, J., (2015). Development of Compositional and Contextual Communication of Robots by using the Multiple Timescales Dynamic Neural Network. Proc. of the Fifth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob2015), August, 176-181. PDF
  9. Hwang, J., Jung, M., Madapana, N., Kim, J., Choi, M., & Tani, J. (2015). Achieving “Synergy” in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination. Proc. of 2015 IEEE-RAS International Conference on Humanoid Robots, 817-824. PDF
  10. Jung, M., Hwang, J., & Tani, J. (2014). Multiple Spatio-Temporal Scales Neural Network for Contextual Visual Recognition of Human Actions. Proc. of the Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob2014), Genoa, Italy, October, 227-233. PDF
  11. Murata, S., Yamashita, Y., Arie, H., Ogata, T., Tani, J., & Sugano. S. (2014). Generation of Sensory Reflex Behavior versus Intentional Proactive Behavior in Robot Learning of Cooperative Interactions with Others. In Proceedings of the Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2014), Genoa, Italy, October, 234-240.
  12. Tan, B.H., Tang, H., Yan, R., & Tani, J. (2014). A Flexible and Robust Robotic Arm Design and Skill Learning by Using Recurrent Neural Networks. In Proc. of IEEE Int. Conf. on Intelligent Robots and Systems (IROS2014), September, 522-529.
  13. Murata, S., Arie, H., Ogata, T., Tani, J., & Sugano, S. (2014). Learning and Recognition of Multiple Fluctuating Temporal Patterns Using S-CTRNN. The 24th International Conference on Artificial Neural Networks (ICANN 2014), Hamburg, Germany, September, 9-16.
  14. Tani, J., Maniadakis, M., & Paine, RW. (2014). Understanding Higher-Order Cognitive Brain Mechanisms by Conducting Evolutional Neuro-robotics Experiments. In The Horizons of Evolutionary Robotics, ed., P.A. Vargas, E.A. Di Paolo, I. Harvey and P. Husband, MIT Press, 219-236.
  15. Murata, S., Namikawa, J., Arie, H., Tani, J., & Sugano, S. (2013). Development of Proactive and Reactive Behavior via Meta-Learning of Prediction Error Variance. The 20th International Conference on Neural Information Processing, Daegu, Korea, November, 537-544.
  16. Murata, S., Namikawa, J., Arie, H., Tani, J., & Sugano, H. (2013). Learning to Reproduce Fluctuating Behavioral Sequences Using a Dynamic Neural Network Model with Time-Varying Variance Estimation Mechanism. The Third Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, Osaka, Japan, August, 1-6.
  17. Nishide, S., Tani, J., Okuno, H.G., & Ogata, T. (2012). Self-organization of Object Features Representing Motion Using Multiple Timescales Recurrent Neural Network. Proc. of Int. Joint. Conf. of Neural Networks (IJCNN2012), June.
  18. Komatsu, M., Namikawa, J., Tani, J., Chao, C.Z., Nagasaka, Y., Fujii, N., & Nakamura, K. (2012): Estimation of functional brain connectivity from electrocorticograms using an artificial network model. Proc. of Int. Joint. Conf. of Neural Networks (IJCNN2012), June.
  19. Alnajjar, F., Yamashita, Y., & Tani, J. (2011). Formulating a Cognitive Branching Task by MTRNN:A Robotic Neuroscience Experiments to Simulate the PFC and its Neighboring Regions. Advances in Cognitive Neurodynamics (III): Proceedings of the Third International Conference on Cognitive Neurodynamics , 267-274.
  20. Yamashita, Y., & Tani, J. (2011). Neurodynamical account for altered awareness of action in schizophrenia: a synthetic neuro-roboic study. Advances in Cognitive Neurodynamics (III): Proceedings of the Third International Conference on Cognitive Neurodynamics, 275-280.
  21. Namikawa, J., Nishimoto, R., Arie, H., & Tani, J. (2011). Synthetic approach to understanding meta-level cognition of predictability in generating cooperative behavior. Advances in Cognitive Neurodynamics (III): Proceedings of the Third International Conference on Cognitive Neurodynamics, 615-611.
  22. Maniadakis, M., Tani, J., & Trahanias, P. (2011). Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks. Proc. IEEE Int. Conf. on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Frankfurt, Germany, August.
  23. Peniak, M., Marocco, D., Tani, J., Yamashita, Y., Fischer, K., & Cangelosi, A. (2011). Multiple time scales recurrent neural network for complex action acquisition. Proc. IEEE Int. Conf. on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Frankfurt, Germany, August.
  24. Nishide, S., Tani, T., Okuno, H.G., & Ogata, T. (2011). Handwriting prediction based character recognition using recurrent neural network. 2011 IEEE Int. Conf. on Sysntems, Man, and Cybernetics, Anchorage, USA, October, 2549-2554.
  25. Nishimoto, R., & Tani, J. (2011). Schemata Learning. In Perception-Action Cycle, Springer New York, 219-241.
  26. Jeong, S., Park, Y., Arie, H., Tani, J., & Lee, M. (2011). Goal-oriented behavior generation for visually-guided manipulation task. Lecture Notes in Computer Science, 7062, 501-508, Proc. 18th Int. Conf, ICONIP 2011, Shanghai, China, November.
  27. Awano, H., Nishide, S., Arie, H., Tani, J., Takahashi, T., Okuno, H.G., & Ogata, T. (2011). Use of a sparse structure to improve learning performance of recurrent neural networks. Lecture Notes in Computer Science, 7064, 323-331; Proc. 18th Int. Conf, ICONIP 2011, Shanghai, China, November.
  28. Nishide, S., Ogata, T., Tani, J., Takahashi, T., Komatani, K., & Okuno, HG. (2010). Motion generation based on reliable predictability using self-organized object features. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2010), Taipei, Taiwan, October, 3453-3458.
  29. Arie, H., Endo, T., Jeong, S., Lee, M., Sugano, S., & Tani, J. (2010). Integrative learning between language and action: a neuro-robotics experiment. Lecture Notes in Computer Science, 6353, 256-265: Proc. 20th Int. Conf. on Artificial Neural Networks (ICANN2010), Thessaloniki, Greece, September. PDF
  30. Jeong, S., Lee, M., Arie, H., & Tani, J. (2010). Developmental learning of integrating visual attention shifts and bimanual object grasping and manipulation tasks. Proc. IEEE 9th Int. Conf. on Development and Learning (ICDL2010), Ann Arbor, USA, August, 165-170. PDF
  31. Maniadakis, M., Trahanias, P., & Tani, J. (2010). Self-organized executive control functions. Proc. 2010 Int. Joint Conf. on Neural Networks (IJCNN2010), Barcelona, Spain, July, 3633-3640. PDF
  32. Hinoshita, W., Arie, H., Tani, J., Ogata, T., & Okuno, H.G. (2010). Recognition and generation of sentences through self-organizing linguistic hierarchy using MTRNN. Lecture Notes in Artificial Intelligence, 6098, 42-51; Proc. 23rd Int. Conf. on Industrial Engineering and Other Applications of Applied Intelligence Systems (IEA/AIE2010), Cordoba, Spain, June.
  33. Arie, H., Endo, T., Arakaki, T., Sugano, S., & Tani, J. (2009). A neuro-dynamical model for understanding mechanisms of goal-directed action imitation. Proc. 3rd Int. Symp. on Mobiligence, Awaji, Japan, November, 46-51.
  34. Namikawa, J., & Tani, J. (2009). Learning to generate probabilistic event transition sequences via chaotic dynamics. Proc. 3rd Int. Symp. on Mobiligence, Awaji, Japan, November, 129-132.
  35. Nishide, S., Nakagawa, T., Ogata, T., Tani, J., Takahashi, T., & Okuno, H.G. (2009). Modeling tool-body assimilation using second-order recurrent neural network. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2009), St. Louis, USA, October, 5376-5381. PDF
  36. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2009). Analysis of motion searching based on reliable predictability using recurrent neural network. Proc. 2009 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM2009), Singapore, July, 192-197.
  37. Namikawa, J., & Tani, J. (2009). Learning to imitate stochastic time series in a compositional way by chaos. Proc. IEICE Technical Report on Neurocomputing, Ikoma, Japan, July, 109(125), 25-30.
  38. Maniadakis, M., Tani, J., & Trahanias, P. (2009). Time perception in shaping cognitive neurodynamics of artificial agents. Proc. 2009 Int. Joint Conf. on Neural Networks (IJCNN2009), Atlanta, USA, June, 1993-2000.
  39. Rybicki, L., Sugita, Y., & Tani, J. (2009). Reinforcement learning of multiple tasks using parametric bias. Proc. 2009 Int. Joint Conf. on Neural Networks (IJCNN2009), Atlanta, USA, June, 2732-2739.
  40. Nishimoto, R., & Tani, J. (2009). Development process of functional hierarchy for actions and motor imagery. Proc. IEEE 8th Int. Conf. on Development and Learning (ICDL2009), Shanghai, China, June.
  41. Arie, H., Endo, T., Arakaki, T., Sugano, S., & Tani, J. (2009). Creating novel goal-directed actions using chaotic dynamics. Proc. IEEE 8th Int. Conf. on Development and Learning (ICDL2009), Shanghai, China, June, 1-6.
  42. Ogata, T., Yokoya, R., Tani, J., Komatani, K., & Okuno, H.G. (2009). Prediction and imitation of other's motions by reusing own forward-inverse model in robots. Proc. 2009 IEEE Int. Conf. on Robots and Automation (ICRA2009), Kobe, Japan, May, 4144-4149.
  43. Tani, J. (2008). Co-developmental learning between humanoids and human via force and intentionality interaction. Proc. 4th Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS2008), Munich, Germany.
  44. Nishide, S., Ogata, T., Yokoya, R., Tani, J., Komatani, K., & Okuno, H.G. (2008). Active sensing based dynamical object feature extraction. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2008), Nice, France, September, 1-7. PDF
  45. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2008). Analysis of reliable predictability based motion generation using RNNPB. Proc. Int. Conf. on Soft Computing and Intelligent Systems and Int. Symposium on advanced Intelligent Systems (SCIS&ISIS2008), Nogoya, Japan, September, 305-310. PDF
  46. Sugita, Y., & Tani, J. (2008). A sub-symbolic process underlying the usage-based acquisition of a compositional representation: Results of robotic learning experiments of goal-directed actions. Proc. 7th IEEE Int. Conf. on Development and Learning (ICDL2008), Monterey, USA, August, 127-132. PDF
  47. Maniadakis, M., & Tani, J. (2008). Dynamical systems account for meta-level cognition. Lecture Notes in Artificial Intelligence, 5040, 311-320, Proc. 10th Int. Conf. on Simulation of Adaptive Behavior (SAB2008), Osaka, Japan, July.
  48. Sugita, Y., & Tani, J. (2008). Acquiring a functionally compositional system of goal-directed actions of a simulated agent. Lecture Notes in Artificial Intelligence, 5040, 331-341, Proc. 10th Int. Conf. on Simulation of Adaptive Behavior (SAB2008), Osaka, Japan, July. PDF
  49. Nishide, S., Ogata, T., Yokoya, R., Tani, J., Komatani, K., & Okuno, H.G. (2008). Object dynamics prediction and motion generation based on reliable predictability. Proc. IEEE-RAS Int. Conf. on Robots and Automation (ICRA2008), Pasadena, USA, May, 1608-1614. PDF
  50. Yokoya, R., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2007). Discovery of other individuals by projecting a self-model through imitation. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2007), San Diego, USA, October-November, 1009-1014.
  51. Ogata, T., Murase, M., Tani, J., Komatani, K., & Okuno, H.G. (2007). Two-way translation of compound sentences and arm motions by recurrent neural networks. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2007), San Diego, USA, October-November, 1858-1863. PDF
  52. Arie, H., Sugano, S., & Tani, J. (2007). Constructive approach to understanding the active learning process of adaptation within a given task environment. Proc. 2nd Int. Symp. on Mobiligence, Awaji, Japan, July, 77-80.
  53. Yamashita, Y., Okumura, T., Okanoya, K., & Tani, J. (2007). Function of the sensori-motor nucleus NIf in generation of complex syntactical song in the Bengalese Finch. Proc. 2nd Int. Symp. on Mobiligence, Awaji, Japan, July, 101-104.
  54. Nishide, S., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2007). Predicting Object Dynamics from Visual Images through Active Sensing Experiences. Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA2007), Roma, Italy, April, 2501-2506. PDF
  55. Ogata, T., Matsumoto, S., Tani, J., Komatani, K., & Okuno, H.G. (2007). Human-Robot Cooperation using Quasi-symbols Generated by RNNPB Model. Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA2007), Roma, Italy, April, 2156-2161.
  56. Arie, H., Namikawa, J., Ogata, T., Tani, J., & Sugano, S. (2006). Reinforcement learning algorithm with CTRNN in continuous action space. Lecture Notes in Computer Science, 4232, 387-396; Int. Conf. on Neural Information Processing (ICONIP2006), Hong Kong, China, October.
  57. Yokoya, R., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2006). Experience Based Imitation Using RNNPB. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2006), Beijing, China, October, 3669-3674.
  58. Noda, K., Ito, M., Hoshino, Y., & Tani, J. (2006). Dynamic generation and switching of object handling behaviors by a humanoid robot using a recurrent neural network model. Lecture Notes in Artificial Intelligence, 4095, 185-196; Int. Conf. on the Simulation of Adaptive Behavior (SAB’06), Rome, Italy, September.
  59. Igari, I., Hirata, C., & Tani, J. (2006). Computational model for sequence learning: generalization and differentiation dynamics of learning modules. Proc. 5th Int. Conf. on Development and Learning (ICDL2006), Bloomington, USA, May-June, 45-1~45-6.
  60. Yokoya, R., Ogata, T., Tani, J., Komatani, K., & Okuno, H.G. (2006). Robot imitation from Active-sensing experiences. Proc. 5th Int. Conf. on Development and Learning (ICDL2006), Bloomington, USA, May-June, 27-1~27-6.
  61. Tani, J. (2005). Self-organization of neuronal dynamical structures through sensory-motor experiences of robots. Proc. Workshop on Intelligence Dynamics, IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids2005), Tsukuba, Japan, December, 32-37.
  62. Ogata, T., Ohba, H., Tani, J., Komatani, K., & Okuno, H.G. (2005). Extracting multi-modal dynamics of objects using RNNPB. Proc. 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2005), Edmonton, Canada, August, 160-165.
  63. Sugita, Y., & Tani, J. (2005). Learning segmentation of behavior to acquire situated combinatorial semantics. Proc. Workshop on Neural-Symbolic Learning and Reasoning, 19th Int. Joint Conf. on Artificial Intelligence (IJCAI-05), Edinburgh, UK, August, 1-6.
  64. Tani, J., & Ito, M. (2004). Interacting with NeuroCognitive Robots: A Dynamical System View. Proc. 2nd Int. Workshop on Man-Machine Symbiotic Systems, Kyoto, Japan, November, 123-134. PDF
  65. Ogata, T., Sugano, S., & Tani, J. (2004). Acquisition of Motion Primitives of Robot in Human-Navigation Task: Towards Human-Robot Interaction based on ‘Quasi-Symbol. Proc. 2nd Int. Workshop on Man-Machine Symbiotic Systems, Kyoto, Japan, November, 315-326.
  66. Ito, M., & Tani, J. (2004). Generalization in Learning Multiple Temporal Patterns Using RNNPB. Proc. 11th Int. Conf. on Neural Information Processing (ICONIP2004), Calcutta, India, edited by Pal N.R., Kasabov N., Mudi R.K., Pal S., Parui S.K., Springer-Verlag, November, 3316, 592-598. PDF
  67. Tani, J., Ito, M., & Sugita, Y. (2004). Review of a dynamic neural network scheme for synthesizing cognition of robots and humanoids. Proc. IEEE-RAS/RSJ Int. Conf. on Humanoid Robots (Humanoids2004), Los Angeles, USA, November, CD1-20.
  68. Ito, M., & Tani, J. (2004). Joint attention between a humanoid robot and users in imitation game. Proc. 3rd Int. Conf. on Development and Learning (ICDL'04), La Jolla, USA, October.  PDF
  69. Ogata, T., Matsunaga, M., Sugano, S., & Tani, J. (2004). Human-robot collaboration using behavioral primitives. Proc. 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2004), Sendai, Japan, September-October, 1592-1597. PDF
  70. Ogata, T., Sugano, S., & Tani, J. (2004). Open-end human robot interaction from the dynamical systems perspective: Mutual adaptation and incremental learning. Lecture Notes in Artificial Intelligence, 3029, 435-444; Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE2004), Ottawa, Canada, May.
  71. Sugita, Y., & Tani, J. (2004). A holistic approach to compositional semantics: a connectionist model and robot experiments. Advances in Neural Information Processing Systems 16 (NIPS2003), Vancouver and Whistler, Canada, December, edited by Thrun S., Saul L.K., Scholkopf B., The MIT Press, 969-976. PDF
  72. Sugita, Y., & Tani, J. (2004). A connectionist approach to learn association between sentences and behavioral patterns of a robot. Proc. 8th Int. Conf. on Simulation of Adaptive Behavior (SAB’04), Los Angeles, USA, July, edited by Schaal S., Ljspeert A., Billard A., Vijayakumar S., Hallam J., Meyer J., The MIT Press, 467-476.
  73. Paine, R.W., & Tani, J. (2004). Adaptive motor primitive and sequence formation in a hierarchical recurrent neural network. Proc. 8th Int. Conf. on Simulation of Adaptive Behavior (SAB’04), Los Angeles, USA, July, edited by Schaal S., Ljspeert A., Billard A., Vijayakumar S., Hallam J., Meyer J., The MIT Press, 274-283.
  74. Paine, R.W., & Tani< J. (2004). Evolved motor primitives and sequences in a hierarchical recurrent neural network. Proc. Genetic and Evolutionary Computation Conference (GECCO2004), Seattle, USA, June, edited by Deb K., Springer-Verlag, 603-614.
  75. Ito, M., & Tani, J. (2004). On-line imitative interaction with a humanoid robot using a mirror neuron model. Proc. 2004 IEEE Int. Conf. on Robotics & Automation (ICRA2004), New Orleans, USA, April-May, 1071-1076.
  76. Ogata, T., Masago, N., Sugano, S., & Tani, J. (2003). Interactive learning in human-robot collaboration. Proc. 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS2003), Las Vegas, USA, October, 162-167.
  77. Ogata, T., Masago, N., Sugano, S., & Tani, J. (2003). Collaboration development through interactive learning between human and robot. Proc. 3rd Int. Workshop on Epigenetic Robotics, Boston, USA, August, edited by Prince C.G. and others., 99-106.
  78. Nishimoto, R., & Tani, J. (2003). Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems. Lecture Notes in Computer Science, 2686, 422-429; Int. Work-Conf. on Artificial and Natural Neural Networks (IWANN2003), Mao, Menorca, Spain, June.
  79. Tani, J. (2003). Symbols and dynamics in embodied cognition: revisiting a robot experiment. Anticipatory Behavior in Adaptive Learning Systems, edited by Butz M.V., Sigaud O., Gerard P., Springer-Verlag, 167-178. PDF
  80. Tani, J. (2002). Articulation of sensory-motor experiences by ‘Forwarding Forward Model’: From robot experiments to phenomenology. Proc. 7th Int. Conf. on Simulation of Adaptive Behavior (SAB’02), Edinburgh, UK, August, edited by Hallam B., Floreano D., Hayes G., Meyer J., and Hallam J., The MIT Press, 171-180. PDF
  81. Sugita, Y., & Tani, J. (2002). A connectionist model which unifies the behavioral and the linguistic processes: Results from robot learning experiments. Mirror Neurons and the Evolution of Brain and Language, Delmenhorst, Germany, edited by Stamenov M.I. and Gallese V., John Benjamins Publishing, 363-376. ps.Z
  82. Tani, J. (2002). Self-organization of behavioral primitives as multiple attractor dynamics: a robot experiment. Proc. 2002 Int. Joint Conf. on Neural Networks (IJCNN’02), Honolulu, USA, May, 489-494.
  83. Tani, J. (2002). The level organization by ‘Forwarding Forward models’: from robot experiments. Proc. 7th Int. Symp. on Artificial Life and Robotics (AROB7th’02), Beppu, Japan, January, 359-366.
  84. Ikegami, T., & Tani, J. (2001). Chaotic itinerancy needs embodied cognition to explain memory dynamics. Behavioral and Brain Sciences, 24(5), 818-819.
  85. Tani, J., & Sugita, Y. (1999). On the dynamics of robot exploration learning. Proc. 5th European Conf. on Artificial Life (ECAL99), Lausanne, Switzerland, September.
  86. Ito, M., & Tani, J. (1998). Dynamic adaptation of a neural-net based agent. Proc. 8th Int. Conf. on Artificial Neural Networks (ICANN’98), Skovde, Sweden, September, 1151-1156.
  87. Horikawa, K., Asoh, H., & Tani, J. (1998). Emergence of experts modules for mobile robot navigation from a mixture of Elman networks. Proc. Int. Conf. on Neural Information Processing Systems.
  88. Sugita, Y., & Tani, J. (1998). Emergence of cooperative/competitive behavior in two robot’s game: plans or skills?. SAB’98 Workshop on Adaptive Behavior using dynamic recurrent neural nets, Zurich, Switzerland.
  89. Tani, J., & Nolfi, S. (1998). Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Proc. 5th Int. Conf. on Simulation of Adaptive Behavior (SAB’98), Zurich, Switzerland, August, edited by Pfeifer R., Blumberg B., Meyer J-A., Wilson S., The MIT Press, 270-279.
  90. Tani, J. (1997). Visual attention and learning of a cognitive robot. Proc. 7th Int. Conf. on Artificial Neural Networks (ICANN’97), Special session on Adaptive Autonomous Agents, Lausanne, Switzerland.
  91. Tani, J., Yamamoto, J., & Nishi, H. (1997). Dynamical interactions between learning, visual attention, and behavior: an experiment with a vision-based mobile robot. Proc. 4th European Conf. on Artificial Life (ECAL97), Brighton, UK, July, edited by Husbands P. and Harvey I., The MIT Press, 309-317. PDF
  92. Tani, J. (1996). A dynamical systems approach to represent cognition of robots: a view of the internal observer. AAAI Fall Symposium: Embodied Cognition and Action, Cambridge, USA, TR FS-96-02, 123-128.
  93. Tani, J. (1996). Does dynamics solve the symbol grounding problem of robots?. Proc. AISB’96 Workshop: Learning in Robots and Animals, Brighton, UK.
  94. Tani, J., & Fukumura, N. (1995). A dynamical systems approach for a learnable autonomous robot. Advances in Neural Information Processing Systems 8 (NIPS’95), Denver, Colorado, November, edited by Touretzky S.D., Mozer C. M., Hasselmo E.M., The MIT Press, 989-995. PDF
  95. Tani, J. (1995). Self-organization of symbolic processes through interactions with the physical world. Proc. 14th Int. Joint Conf. on Artificial Intelligence (IJCAI’95), Montreal, Canada, August, 112-118. PDF
  96. Tani, J. (1995). Essential dynamical structure in learnable autonomous robots. Proc. 3rd European Conf. on Artificial Life (ECAL95), Granada, Spain, June, Springer-Verlag.
  97. Tani, J. (1995). Embedding symbolic process into deterministic chaos. Proc. Biologically Inspired Evolutionary Systems (BIES95), Tokyo, Japan, 156-162.
  98. Tani, J. (1995). Dynamical systems approach in learnable autonomous robots. Proc. Information Integration Workshop, Beyond Divide and Conquer Strategy (IIW95), 241-249.
  99. Tani, J. (1994). Experiment of Learning and Chaotic Planning of a Mobile Robot. Proc. 2nd Int. Conf. on Fuzzy Logic, Neural Nets and Soft Computing, Iizuka, Japan.
  100. Tani, J., & Fukumura, N. (1994). Embedding task-based behavior into internal sensory-based attractor dynamics in navigation of a mobile robot. Proc. 1994 IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems (IROS’94), Munich, Germany.
  101. Tani, J., & Fukumura, N. (1993). Learning task-based behavior as attractor dynamics: an experiment of autonomous mobile robot. Proc. Int. Symp. on Nonlinear Theory and Its Applications (NOLTA’93), Hawaii, USA, 2, 431-434.
  102. Tani, J., & Fukumura, N. (1993). Learning goal-directed navigation as attractor dynamics for a sensory motor system: an experiment by the mobile robot YAMABICO”. IEEE Proc. Int. Joint Conf. on Neural Networks (IJCNN’93), Nagoya, Japan, 1747-1753.
  103. Tani, J. (1992). Diversity and regularity in chaotic wandering of robot. Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks, Iizuka, Japan.
  104. Tani, J. (1992). The role of chaos in processing language. IEEE Proc. Int. Joint Conf. on Neural Networks (IJCNN’92), Baltimore, USA , 3, 444-449.
  105. Tani, J., Hirobe, T., Niida, K., Koshijima, I., & Murakami, H. (1989). New learning algorithm for rule extraction by neural network and its application. Proceedings of the 4th Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, October 1-6, 35.1-35.16.

MS and Ph.D Theses

  1. Park, G. (2015). Development of Compositional and Contextual Communicative Skills of Robot by Using a Neuro-Dynamic Model. MS Thesis. PDF