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Dissociated Emergent-Response System and Fine-Processing System in Human Neural Network and a Heuristic Neural Architecture for Autonomous Humanoid Robots

DOI: 10.1155/2010/314932

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Abstract:

The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC) were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence. 1. Introduction In the research community on human level intelligence [1, 2], there has been increasing investigation on autonomous agents [3] and humanoid robots [4, 5], for which independent survival is essential. Previously, research efforts have been focused on imitating human cognition and behaviors (for review see [6]), for example, motion, perception, reasoning, and even emotion and social interaction [6–9], but no sufficient attention has been paid upon their sustainability, for example, monitoring and avoiding danger, acknowledging physical harm and threatening, and so forth. The current study hopes to apply the knowledge from the neural network of the human brain into the design of the neural architecture of humanoid robots. To be autonomous, the robot needs to maintain constant monitoring of its outside environment and inside status, which is similar to the function of the primary sensory system of the human brain; it is also better to have an independent processing unit so as to respond quickly in face of danger, which is similar to the role of the amygdala in the human brain. It is also necessary to have an executive center to consolidate the possible conflict between the need for survival-based quick response and the need for thorough computation, similar to the cognitive control role of the anterior cingulate cortex (ACC). Therefore, we hope to gain some insight from the neural architecture of the human brain to help such

References

[1]  H. A. Simon, The Shape of Automation for Men and Management, Harper & Row, New York, NY, USA, 1965.
[2]  N. L. Cassimatis, A Cognitive Substrate for Achieving Human-Level Intelligence, American Association for Artificial Intelligence, La Canada, Calif, USA, 1980.
[3]  S. Franklin and A. Graesser, “Is it an agent, or just a program?: a taxonomy for autonomous agents,” in Proceedings of the 3rd International Workshop on Agent Theories, Architectures, and Languages, 1997.
[4]  R. A. Brooks, “Prospects for human level intelligence for humanoid robots,” in Proceedings of the 1st International Symposium on Humanoid Robots (HURO '96), pp. 17–24, 1996.
[5]  R. Brooks, C. Breazeal, M. Marjanovi, B. Scassellati, and M. Williamson, The Cog Project: Building a Humanoid Robot, Springer, New York, NY, USA, 1999.
[6]  S. Schaal, “Is imitation learning the route to humanoid robots?” Trends in Cognitive Sciences, vol. 3, no. 6, pp. 233–242, 1999.
[7]  B. Scassellati, “Theory of mind for a humanoid robot,” Autonomous Robots, vol. 12, no. 1, pp. 13–24, 2002.
[8]  C. Breazeal, “Emotion and sociable humanoid robots,” International Journal of Human Computer Studies, vol. 59, no. 1-2, pp. 119–155, 2003.
[9]  T. Fong, I. Nourbakhsh, and K. Dautenhahn, “A survey of socially interactive robots,” Robotics and Autonomous Systems, vol. 42, no. 3-4, pp. 143–166, 2003.
[10]  K. J. Friston, “Functional and effective connectivity in neuroimaging: a synthesis,” Human Brain Mapping, vol. 2, no. 1-2, pp. 56–78, 1994.
[11]  B. Horwitz, “The elusive concept of brain connectivity,” NeuroImage, vol. 19, no. 2, pp. 466–470, 2003.
[12]  B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magnetic Resonance in Medicine, vol. 34, no. 4, pp. 537–541, 1995.
[13]  J. S. Hyde and B. B. Biswal, “Functionally related correlation in the noise,” in Functional MRI, C. T. W. Moonen and P. A. Bandettini, Eds., pp. 263–275, Springer, Berlin, Germany, 2000.
[14]  D. Cordes, V. M. Haughton, K. Arfanakis et al., “Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data,” American Journal of Neuroradiology, vol. 22, no. 7, pp. 1326–1333, 2001.
[15]  M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” NeuroImage, vol. 29, no. 4, pp. 1359–1367, 2006.
[16]  M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, “Functional connectivity in the resting brain: a network analysis of the default mode hypothesis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 1, pp. 253–258, 2003.
[17]  M. D. Greicius, K. Supekar, V. Menon, and R. F. Dougherty, “Resting-state functional connectivity reflects structural connectivity in the default mode network,” Cerebral Cortex, vol. 19, no. 1, pp. 72–78, 2009.
[18]  B. B. Biswal, M. Mennes, X.-N. Zuo et al., “Toward discovery science of human brain function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 10, pp. 4734–4739, 2010.
[19]  M. Davis, “The role of the amygdala in fear and anxiety,” Annual Review of Neuroscience, vol. 15, pp. 353–375, 1992.
[20]  R. Adolphs, D. Tranel, H. Damasio, and A. R. Damasio, “Fear and the human amygdala,” Journal of Neuroscience, vol. 15, no. 9, pp. 5879–5891, 1995.
[21]  S. M. Courtney, L. G. Ungerleider, K. Keil, and J. V. Haxby, “Transient and sustained activity in a distributed neural system for human working memory,” Nature, vol. 386, no. 6625, pp. 608–611, 1997.
[22]  J. A. Waltz, B. J. Knowlton, K. J. Holyoak et al., “A system for relational reasoning in human prefrontal cortex,” Psychological Science, vol. 10, no. 2, pp. 119–125, 1999.
[23]  B. A. Vogt, D. M. Finch, and C. R. Olson, “Functional heterogeneity in cingulate cortex: the anterior executive and posterior evaluative regions,” Cerebral Cortex, vol. 2, no. 6, pp. 435–443, 1992.
[24]  O. Devinsky, M. J. Morrell, and B. A. Vogt, “Contributions of anterior cingulate cortex to behaviour,” Brain, vol. 118, no. 1, pp. 279–306, 1995.
[25]  C. S. Carter and V. van Veen, “Anterior cingulate cortex and conflict detection: an update of theory and data,” Cognitive, Affective and Behavioral Neuroscience, vol. 7, no. 4, pp. 367–379, 2007.
[26]  A. W. MacDonald III, J. D. Cohen, V. Andrew Stenger, and C. S. Carter, “Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control,” Science, vol. 288, no. 5472, pp. 1835–1838, 2000.
[27]  R. W. Cox, “AFNI: software for analysis and visualization of functional magnetic resonance neuroimages,” Computers and Biomedical Research, vol. 29, no. 3, pp. 162–173, 1996.
[28]  J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging, Thieme, New York, NY, USA, 1988.
[29]  R. A. Fisher, “Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population,” Biometrika, vol. 10, pp. 507–521, 1915.
[30]  C. W. J. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica, vol. 37, pp. 424–438, 1969.
[31]  M. Ding, Y. Chen, and S. L. Bressler, “Granger causality: basic theory and application to neuroscience,” in Handbook of Time Series Analysis, S. Schelter Mwjt, Ed., pp. 438–460, Wiley, Weinheim, Germany, 2006.
[32]  A. K. Seth, “A MATLAB toolbox for Granger causal connectivity analysis,” Journal of Neuroscience Methods, vol. 186, no. 2, pp. 262–273, 2010.
[33]  J. Cui, L. Xu, S. L. Bressler, M. Ding, and H. Liang, “BSMART: a Matlab/C toolbox for analysis of multichannel neural time series,” Neural Networks, vol. 21, no. 8, pp. 1094–1104, 2008.
[34]  M. J. Lowe, B. J. Mock, and J. A. Sorenson, “Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations,” NeuroImage, vol. 7, no. 2, pp. 119–132, 1998.
[35]  S. Pinker, “Visual cognition: an introduction,” Cognition, vol. 18, no. 1–3, pp. 1–63, 1984.
[36]  D. Marr, “Vision: a computational investigation into the human representation and processing of visual information,” Vision, 1982.
[37]  R. Malach, I. Levy, and U. Hasson, “The topography of high-order human object areas,” Trends in Cognitive Sciences, vol. 6, no. 4, pp. 176–184, 2002.
[38]  N. Kanwisher, J. McDermott, and M. M. Chun, “The fusiform face area: a module in human extrastriate cortex specialized for face perception,” Journal of Neuroscience, vol. 17, no. 11, pp. 4302–4311, 1997.
[39]  R. Malach, J. B. Reppas, R. R. Benson et al., “Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex,” Proceedings of the National Academy of Sciences of the United States of America, vol. 92, no. 18, pp. 8135–8139, 1995.
[40]  L. Cohen, S. Dehaene, L. Naccache et al., “The visual word form area. Spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients,” Brain, vol. 123, no. 2, pp. 291–307, 2000.
[41]  A. S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound, The MIT Press, 1994.
[42]  P. Jacob, M. Jeannerod, C. Heckscher, et al., Ways of Seeing: The Scope and Limits of Visual Cognition, Oxford University Press, Oxford, UK, 2004.
[43]  R. J. Maddock, A. S. Garrett, and M. H. Buonocore, “Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task,” Human Brain Mapping, vol. 18, no. 1, pp. 30–41, 2003.
[44]  V. van Veen and C. S. Carter, “Conflict and cognitive control in the brain,” Current Directions in Psychological Science, vol. 15, no. 5, pp. 237–240, 2006.
[45]  J. V. Pardo, P. J. Pardo, K. W. Janer, and M. E. Raichle, “The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm,” Proceedings of the National Academy of Sciences of the United States of America, vol. 87, no. 1, pp. 256–259, 1990.
[46]  G. Bush, P. Luu, and M. I. Posner, “Cognitive and emotional influences in anterior cingulate cortex,” Trends in Cognitive Sciences, vol. 4, no. 6, pp. 215–222, 2000.
[47]  M. A. Pinsk and S. Kastner, “Neuroscience: unconscious networking,” Nature, vol. 447, no. 7140, pp. 46–47, 2007.
[48]  M. C. Stevens, G. D. Pearlson, and V. D. Calhoun, “Changes in the interaction of resting-state neural networks from adolescence to adulthood,” Human Brain Mapping, vol. 30, no. 8, pp. 2356–2366, 2009.
[49]  W. W. Seeley, R. K. Crawford, J. Zhou, B. L. Miller, and M. D. Greicius, “Neurodegenerative diseases target large-scale human brain networks,” Neuron, vol. 62, no. 1, pp. 42–52, 2009.

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