Laboratory for Behavior and Dynamic Cognition
Laboratory Head
Jun TANI (D.Eng.)
It is considered that most cognitive processes emerge not solely in the brains but in their dynamic interactions with the environment associated with bodily behaviors. Based on this idea of the embodied cognition we attempt to describe the essential mechanism of cognitions coupled with sensory-motor systems using dynamical systems language. For this purpose, we are conducting interdisciplinary studies by taking the synthetic approach with neural network modeling and robotics experiments and the empirical approach including psychological experiments and electro-physiology. The following shows our focus of studies. First, we focus on the problem of how behavior schemata or primitives can be organized in order to generate or recognize various and complex behavior patterns. More specific questions are that (1) whether they are represented locally or distributedly in networks, (2) how they can be represented in a hierarchical way. We propose a novel model on cortical interactions between premotor and parietal cortexes and analyze its functional characteristics by conducting robotics experiments. Second, we work on the problems of symbols and language. Although in the conventional cognitive science symbol representations and their manipulations are assumed as apriori, a crucial question is that how such symbol systems can be realized in the neuronal dynamical systems in brains. It is also asked how they can be acquired as grounded to the sensory-motor experiences.Furthermore, how can meta-level cognitive functions such as self-referential monitoring, task sets and their switching be implemented in neural network dynamics in prefrontal cortex of human? We study these problems by taking a synthetic approach using neural net modeling and robotics experiments. Third, we conduct electro-physiological experiments of Bengalese Finches during their song learning for the purpose of exploring the essential neuronal mechanisms for acquiring behavior sequential structures utilizing hierarchical organization of the nucleus. The finding obtained in these empirical studies would provide novel ideas to the modeling studies mentioned above. Finally, we conduct basic studies for complex adaptive systems in order to gain theoretical understanding of adaptive behaviors in an open environment.
- Developmental learning of object manipulations by robots
- Modeling of premotor-parietal cortical interactions in learning and generating goal-directed skilled actions
- Fundamental researches on continuous-time and space recurrent neural networks
- Prefrontal modeling for meta-level cognition
- Language and behavior unification learning
- November 07, 2008
- Robots show that brain activity is linked to time as well as space
- Namikawa J., Tani J.:
"Learning to imitate stochastic time series in a compositional way by chaos"
Neural Networks, 23, 625-638 (2010) - Maniadakis M., Trahanias P., Tani J.:
"Explorations on artificial time perception"
Neural Networks, 22, 509-517 (2009) - Nishimoto R., Tani J.:
"Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study"
Psychological Research, 73, 545-558 (2009) - Yamashita Y., Tani J.:
"Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment"
PLoS Comput. Biol., 4 (11), e1000220 (2008) - Namikawa J., Tani J.:
"A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance"
Neural Networks, 21, 1466-1475 (2008) - Yamashita Y., Takahashi M., Okumura T., Ikebuchi M., Yamada H., Suzuki M., Okanoya K., Tani J.:
"Developmental learning of complex syntactical song in the Bengalese Finch: a neural network model"
Neural Networks, 21, 1224-1231, (2008) - Tani J., Nishimoto R., Paine RW.:
"Achieving 'organic compositionality' through self-organization: reviews on brain-inspired robotics experiments"
Neural Networks, 21, 584-603 (2008) - Tani J., Nishimoto R., Namikawa J., Ito M.:
"Codevelopmental learning between human and humanoid robot using a dynamic neural-network model"
IEEE Trans. on Systems, Man, and Cybernetics Part B: Cybernetics, 38 (1), 43-59 (2008) - Ito M., Noda K., Hoshino Y., Tani J.:
"Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model"
Neural Networks, 19, 323-337 (2006) - Paine RW., Tani J.:
"How Hierarchical Control Self-organizes in Artificial Adaptive Systems"
Adaptive Behavior, 13, 211-225 (2005)