CREATING NOVEL GOAL-DIRECTED ACTIONS AT CRITICALITY: A NEURO-ROBOTIC EXPERIMENT

The present study examines the possible roles of cortical chaos in generating novel actions for achieving specified goals. The proposed neural network model consists of a sensory-forward model responsible for parietal lobe functions, a chaotic network model for premotor functions and prefrontal cortex model responsible for manipulating the initial state of the chaotic network. Experiments using humanoid robot were performed with the model and showed that the action plans for satisfying specific novel goals can be generated by diversely modulating and combining prior-learned behavioral patterns at critical dynamical states. Although this criticality resulted in fragile goal achievements in the physical environment of the robot, the reinforcement of the successful trials was able to provide a substantial gain with respect to the robustness. The discussion leads to the hypothesis that the consolidation of numerous sensory-motor experiences into the memory, meditating diverse imagery in the memory by cortical chaos, and repeated enaction and reinforcement of newly generated effective trials are indispensable for realizing an open-ended development of cognitive behaviors.

[1]  M. Goldberg,et al.  Ventral intraparietal area of the macaque: anatomic location and visual response properties. , 1993, Journal of neurophysiology.

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  Jun Tani,et al.  Model-based learning for mobile robot navigation from the dynamical systems perspective , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[5]  A. Luria Higher Cortical Functions in Man , 1980, Springer US.

[6]  Stanley J. Rosenschein,et al.  A dynamical systems perspective on agent-environment interaction , 1996 .

[7]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[8]  J A Kelso,et al.  Dynamic pattern generation in behavioral and neural systems. , 1988, Science.

[9]  Toshio Inui,et al.  Lateralization of the Posterior Parietal Cortex for Internal Monitoring of Self- versus Externally Generated Movements , 2007, Journal of Cognitive Neuroscience.

[10]  Y. Kuniyoshi,et al.  Emergence and development of embodied cognition: a constructivist approach using robots. , 2007, Progress in brain research.

[11]  K. Doya,et al.  Memorizing oscillatory patterns in the analog neuron network , 1989, International 1989 Joint Conference on Neural Networks.

[12]  J. Assad,et al.  Dissociation of visual, motor and predictive signals in parietal cortex during visual guidance , 1999, Nature Neuroscience.

[13]  Ichiro Tsuda,et al.  Chaotic itinerancy , 2013, Scholarpedia.

[14]  J Saarinen,et al.  Self-Organized Formation of Colour Maps in a Model Cortex , 1985, Perception.

[15]  G. Rizzolatti,et al.  Premotor cortex and the recognition of motor actions. , 1996, Brain research. Cognitive brain research.

[16]  R. Johansson,et al.  Evidence for the involvement of the posterior parietal cortex in coordination of fingertip forces for grasp stability in manipulation. , 2003, Journal of neurophysiology.

[17]  Jun Tani,et al.  An Interpretation of the "Self" From the Dynamical Systems Perspective: A Constructivist Approach , 1998 .

[18]  Ichiro Tsuda,et al.  Memory Dynamics in Asynchronous Neural Networks , 1987 .

[19]  G. Hesslow Conscious thought as simulation of behaviour and perception , 2002, Trends in Cognitive Sciences.

[20]  W. Freeman How Brains Make Up Their Minds , 1999 .

[21]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[22]  W. Freeman,et al.  How brains make chaos in order to make sense of the world , 1987, Behavioral and Brain Sciences.

[23]  D. Turcotte,et al.  Self-organized criticality , 1999 .

[24]  W. Penfield,et al.  THE FRONTAL LOBE IN MAN: A CLINICAL STUDY OF MAXIMUM REMOVALS , 1935 .

[25]  M. Jeannerod The representing brain: Neural correlates of motor intention and imagery , 1994, Behavioral and Brain Sciences.

[26]  C. S. Hallpike,et al.  STUDIES IN HUMAN VESTIBULAR FUNCTIONI. OBSERVATIONS ON THE DIRECTIONAL PREPONDERANCE (“NYSTAGMUSBEREITSCHAFT”) OF CALORIC NYSTAGMUS RESULTING FROM CEREBRAL LESIONS , 1942 .

[27]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..

[28]  Toshio Inui,et al.  Differences Between Actual and Imagined Usage of Chopsticks: an FMRI Study , 2007, Cortex.

[29]  Jun Tani,et al.  Learning Multiple Goal-Directed Actions Through Self-Organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment , 2008, Adapt. Behav..

[30]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[31]  Jun Tani,et al.  Achieving "organic compositionality" through self-organization: Reviews on brain-inspired robotics experiments , 2008, Neural Networks.

[32]  Tom Ziemke On 'Parts' and 'Wholes' of Adaptive Behavior: Functional Modularity and Diachronic Structure in Recurrent Neural Robot Controllers , 2000 .

[33]  R. Mizen The embodied mind. , 2009, The Journal of analytical psychology.

[34]  Kenneth M. Heilman,et al.  Intentional motor disorders. , 1991 .