Embodied Intelligence Laboratory

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General philosophy:

J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur and E. Thelen, "Autonomous Mental Development by Robots and Animals,'' Science, vol. 291, no. 5504, pp. 599 - 600, Jan. 26, 2001. PDF file.
J. Weng, ``The Developmental Approach to Intelligent Robots,'' in Proc. 1998 AAAI Spring Symposium Series, Integrating Robotic Research: Taking The Next Leap , Stanford University, March 23-25, 1998. PDF file.
J. Weng, ``The Living Machine Initiative,'' Technical Report MSU-CPS-96-60, Department of Computer Science, MSU, December 1996. PDF file.  The revised version appeared in J. Weng, ``Learning in Computer Vision and Beyond: Development,'' in C. W. Chen and Y. Q. Zhang (eds.),  ``Visual Communication and Image Processing , Marcel Dekker Publisher, New York, NY, 1999. PDF file.

Theory about brain-mind:

J. Weng, "Three Theorems: Brain-Like Networks Logically Reason and Optimally Generalize," in Proc. Int'l Joint Conference on Neural Networks, San Jose, CA, pp. +1-8, July 31 - August 5, 2011. PDF file. (Coined the new terms "symbolic network", "emergent network", "Developmental Network (DN)", "Generative DN (GDN)", and Agent Finite Automaton (AFA). Claimed, with the proofs under review by a journal, that (1) a GDN can learn any AFA incrementally, immediately and error free, (2) when such a DN is frozen for new experience it is optimal in the sense of maximal likelihood, and (3) when such a DN is allowed to continue to learning for new real-world experience it thinks optimally in the sense of maximal likelihood.)
J. Weng, "A 5-Chunk Developmental Brain-Mind Network Model for Multiple Events in Complex Backgrounds,''  International Joint Conference on Neural Networks, July 18-23, Barcelona, Spain, pp. 1-8, 2010. PDF file. (The first brain-mind model in the 5-chunk scale: development, architecture, area, space and time.) DOI: 10.1109/IJCNN.2010.5596740
J. Weng and W. S. Hwang, "From Neural Networks to the Brain: Autonomous Mental Developmen,t''  IEEE Computational Intelligence Magazine, vol. 1, no. 3, pp. 15-31, August 2006. PDF file.

Task muddiness and performance matrics:

Mental Architecture:

LCA: A single-level model as the general-purpose building block of brain networks:

J. Weng and M. Luciw, "Dually Optimal Neuronal Layers: Lobe Component Analysis," IEEE Transactions on Autonomous Mental Development, vol. 1, no. 1, pp. 68-85, 2009. (This is the archival version of LCA: each level of the brain network takes two sources of input --- ascending and descending --- and is optimal in minimizing the error of representing these two sources of input using its limited neuronal resource and limited training experience.) PDF file.
J. Weng and N. Zhang, ``In-Place Learning and the Lobe Component Analysis,''  in Proc. IEEE World Congress on Computational Intelligence, International Joint Conference on Neural Networks, Vancouver, BC, Canada, pp. 1-8, July 16-21, 2006. PDF file.
Matlab program.  Program package with test sets. 
J. Weng, Y. Zhang and W. Hwang, ``Candid Covariance-free Incremental Principal Component Analysis,'' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 1034-1040, 2003. PDF file.

Spatial Processing and Spatial Attention (top-down and bottom-up):

Y. Wang and X. Wu and J. Weng, "Synapse Maintenance in the Where-What Network," in Proc. Int'l Joint Conference on Neural Networks, San Jose, CA, pp. 2823-2829, July 31 - August 5, 2011. PDF file. (This seems the first computational model about synapse maintenance so that each neuron can segment object along its contour to get rid of the background.)
Z. Ji, M. Luciw, J. Weng, and S. Zeng, "Incremental Online Object Learning in a Vehicular Radar-Vision Fusion Framework," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 402-411, June 2011. PDF file. (Online object learning system based on radar-vision fusion framework. Topographic class grouping is utilized to develop discriminative features on V1-coded representation, rather than original pixels, leading to the better performance in vehicle identification.)
M. Luciw and J. Weng, "Top-Down Connections in Self-Organizing Hebbian Networks: Topographic Class Grouping,'' IEEE Transactions on Autonomous Mental Development, vol. 2, no. 3, pp. 248-261, September, 2010.(Brain-inspired deep learning using both bottom-up and top-down connections; why using top-down connections result in discriminative features.)
M. Luciw and J. Weng, "Where What Network 3: Developmental Top-Down Attention with Multiple Meaningful Foregrounds" in Proc. International Joint Conference on Neural Networks, Barcelona, Spain, pp. 4233-4240, July 18-23, 2010. PDF file. (Extension to multiple objects in complex backgrounds, type-based top-down attention, location-based top-down attention, and homeostatic top-down attention.)
Z. Ji and J. Weng, "WWN-2: A Biologically Inspired Neural Network for Concurrent Visual Attention and Recognition,'' in Proc. International Joint Conference on Neural Networks, Barcelona, Spain, pp. 4247-4254, July 18-23, 2010. PDF file. (A single object in complex background, further extends to the free-viewing mode, all pixel locations.)
J. Weng, "A Theory of Architecture for Spatial Abstraction," in Proc. IEEE 8th International Conference on Development and Learning,'' Shanghai, China, pp. +1-8, June 4-7, 2009. PDF file. (Spatial theory, why top-down projections improves the fring sensitivity for relavant components of bottom-up inputs and make the firing less sensitive to irrelavent components.)
M. Luciw and J. Weng, "Laterally connected lobe component analysis: Precision and topography," in Proc. IEEE 8th International Conference on Development and Learning, Shanghai, China, June 4-7, 2009. PDF file. (Top-down connecitons enable clusters for the same class to group in the itnernal representation.)
M. Luciw, J. Weng, S. Zeng, "Motor Initiated Expectation through Top-Down Connections as Abstract Context in a Physical World," in Proc. 7th IEEE International Conference on Development and Learning, Monterey, CA, pp. 115-120, Aug. 9-12, 2008. PDF file. (Top-down connections enable classifications for disjoint tests to become almost perfect.)
Z. Ji, J. Weng, and D. Prokhorov, ``Where-What Network 1: Where and What Assist Each Other Through To
p-down Connections''  in Proc. 7th International Conference on Development and Learning (ICDL'08), Monterey, CA, pp. 61-66,, Aug. 9-12, 2008. PDF file. (From location to type, from type to location, single object, natural background.)

Temporal Processing and Temporal Attention (top-down and bottom-up):

J. Weng, "Brain-Like Temporal Processing', in Y. Meng and Y. Jin (eds.) Bio-Inspired Self-Organizing Robotic Systems, Springer-Verlag, Berlin Heidelberg, pp. 196-212, ISBN 978-3-642-20759-4, 2011.
K. Miyan and J. Weng, ``WWN-Text: Cortex-Like Language Acquisition with ’What’ and ’Where’,'' in Proc. IEEE 9th International Conference on Development and Learning,'' Ann Arbor, pp. 280-285, August 18-21, 2010 . PDF file. (text as perception, early language learning and early language generalization.)
J. Weng and M. Luciw, Online Learning for Attention, Recognition, and Tracking by a Single Developmental Framework, in Proc. 23rd IEEE Conference on Computer Vision and Pattern Recognition, 4th IEEE Online Learning for Computer Vision, pp +1-8, June 13, 2010. PDF file. (Temporal tracking as a special case of type-based attention in general-purpose object recognition from complex backgrounds.)
J. Weng, "A General Purpose Brain Model For Developmental Robots: The Spatial Brain for Any Temporal Lengths," in Proc. IEEE International Conference on Robotics and Automation, Workshop on Bio-Inspired Self-Organizing Robotic Systems, Anchorage, Alaska, pp.+1- 6, May 3-8, 2010. PDF file. (Temporal theory.)
J. Weng, Q. Zhang, M. Chi, and X. Xue, "Complex Text Processing by the Temporal Context Machines," in Proc. IEEE 8th International Conference on Development and Learning," Shanghai, China, pp. +1-8, June 4-7, 2009. PDF file. (Temporal text.)
M. Solgi and J. Weng, ``Developmental Stereo: Emergence of Disparity Preference in Models of Visual Cortex,'' IEEE Transactions on Autonomous Mental Development, vol. 1, no. 4, pp. 238-252, 2009. PDF file. (Temporal stereo without explicit stereo matching, with analysis.)
M. Solgi and J. Weng, "Temporal information as top-down context in binocular disparity detection," in Proc. IEEE 8th International Conference on Development and Learning," Shanghai, China, pp. +1-7, June 4-7, 2009. PDF file. (Temporal stereo without explicit stereo matching.)
M. Luciw, J. Weng, S. Zeng, "Motor Initiated Expectation through Top-Down Connections as Abstract Context in a Physical World," IEEE International Conference on Development and Learning, Monterey, CA, pp. 115-120, Aug. 9-12, 2008. PDF file. (Temporal vision.)

Where-What Networks (WWN-1 to WWN-5):

Y. Wang, X. Wu, X. Song, W. Zhang, J. Weng. Where-What Network with CUDA: General Object Recognition and Location in Complex Backgrounds. In Proc. 2011 International Symposium on Neural Networks (ISNN 2011), Guilin, China, pp. +1-10, May 29 - June 1, 2011, Lecture Notes in Computer Science, 2011, Volume 6676/2011, 331-341. (GPU version of the WWNs.)
X. Song, W. Zhang, J. Weng. "Where-What Networks 5: Dealing with Scales for Objects in Complex Backgrounds," in Proc. 2011 International Joint Conference on Neural Networks, San Jose, California, USA, pp. 2795-2802, July 31 - August 5, 2011. PDF file. (This version deals with objects with multiple scales in complex backgrounds.)
M. Luciw and J. Weng, "Where What Network 4: The Effect of Multiple Internal Areas," in Proc. IEEE 9th International Conference on Development and Learning, Ann Arbor, pp. 311-316, August 18-21, 2010. PDF file. (This seems the first example that showed that deep learning as a cascade of areas between the sensory port and the motor port does not perform as well as shallow learning --- multiple areas each having connections with both ports.)
M. Luciw and J. Weng, "Where What Network 3: Developmental Top-Down Attention with Multiple Meaningful Foregrounds" in Proc. International Joint Conference on Neural Networks, Barcelona, Spain, pp. 4233-4240, July 18-23, 2010. PDF file. (Extension to multiple objects in complex backgrounds, type-based top-down attention, location-based top-down atenton, and homeostatic top-down attention.)
Z. Ji and J. Weng, "WWN-2: A Biologically Inspired Neural Network for Concurrent Visual Attention and Recognition,'' in Proc. International Joint Conference on Neural Networks, Barcelona, Spain, pp. 4247-4254, July 18-23, 2010. PDF file. (A single object in complex background, further extends to the free-viewing mode, all pixel locations.)
Z. Ji, J. Weng, and D. Prokhorov, ``Where-What Network 1: Where and What Assist Each Other Through Top-down Connections''  in Proc. 7th International Conference on Development and Learning (ICDL'08), Monterey, CA, Aug. 9-12, pp. 1-6, 2008. PDF file. (From location to type, from type to location, single object, natural background.)

Modulatory system, motivational system, reinforcement learning:

J. Daly and J. Brown and J. Weng, "Neuromorphic Motivated Systems, in Proc. Int'l Joint Conference on Neural Networks," San Jose, CA, pp. 2917-2914, July 31 - August 5, 2011. PDF file. (This seems the first neuromorphic model for fully emergent serotonin and dopamine systems with navigation as experiments. Prior reinforcement learning systems are either symbolic or not fully neuromorphic.)
S. Paslaski and C. VanDam and J. Weng, "Modeling Dopamine and Serotonin Systems in a Visual Recognition Network," in Proc. Int'l Joint Conference on Neural Networks, San Jose, CA, pp. 3016-3023, July 31 - August 5, 2011. PDF file. (This seems the first neuromorphic model for fully emergent serotonin and dopamine systems with navigation as experiments. Prior reinforcement learning systems are either symbolic or not fully neuromorphic.)
X. Huang and J. Weng, ``Inherent Value Systems for Autonomous Mental Development,'' International Journal of Humanoid Robotics, vol. 4, no. 2, pp. 407-433, 2007.   PDF file.
X. Huang and J. Weng, "Novelty and Reinforcement Learning in the Value System of Developmental Robots,"  in Proc. Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Edinburgh, Scotland, August 10 - 11, 2002. PDF file.

Skill transfer:

Y. Zhang and J. Weng, ``Task Transfer by a Developmental Robot,'' IEEE Transactions on Evolutionary Computation, vol. 11, no. 2, pp. 226-248, 2007. PDF file.
Y. Zhang and J. Weng, ``Action Chaining by a Developmental Robot with a Value System,''  in Proc. 2nd International Conference on Development and Learning, June 12 - 15, MIT, Cambridge, MA, IEEE Computer Society Press, 2002. PDF file.

Theory about developmental robotics:

J. Weng, "Developmental Robotics: Theory and Experiments''  International Journal of Humanoid Robotics, vol. 1, no. 2, 2004. PDF file.
J. Weng, "A Theory for Mentally Developing Robots,''  in Proc. 2nd International Conference on Development and Learning, June 12 - 15, MIT, Cambridge, MA, IEEE Computer Society Press, 2002. PS file or PDF file.

Overview of related projects at the EI Lab:

J. Weng and Y. Zhang, ``Developmental Robots: A New Paradigm,''  an invited paper in Proc. Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Edinburgh, Scotland, August 10 - 11, 2002.  PDF file.

MILN: Cortex inspired sensorimotor pathway:

J. Weng, H. Lu, T. Luwang and X. Xue, ``Multilayer In-place Learning Networks for Modeling Functional Layers in the Laminar Cortex''  Neural Networks, vol. 21, no.2-3, pp. 150-159, 2008. PDF file.
J. Weng, H. Lu, T. Luwang and X. Xue, ``A Multilayer In-Place Learning Network for Development of General Invariances,''  International Journal of Humanoid Robotics, vol. 4, no. 2, pp. 281-320, 2007. PDF file.
J. Weng, H. Lu, T. Luwang and X. Xue, ``In-Place Learning for Positional and Scale Invariance,''  in Proc. IEEE World Congress on Computational Intelligence , International Joint Conference on Neural Networks, Vancouver, BC, Canada, July 16-21, pp. 1-10, 2006. PDF file.
J. Weng and Matthew D. Luciw, ``Optimal In-Place Self-Organization for Cortical Development: Limited Cells, Sparse Coding and Cortical Topology,''  in Proc. 5th International Conference on Development and Learning (ICDL'06) , Bloomington, IN, USA, May 31 - June 3, pp. 1-7, 2006. PDF file.

Sensory mapping engine (early processing for local feature extraction with receptive fields):

N. Zhang, J. Weng and Z. Zhang, ``A Developing Sensory Mapping for Robots,''  in Proc. 2nd International Conference on Development and Learning, June 12 - 15, MIT, Cambridge, MA, IEEE Computer Society Press, 2002. PDF file.
J. Weng and M. D. Luciw, ``Optimal In-Place Self-Organization for Cortical Development: Limited Cells, Sparse Coding and Cortical Topography,''  in Proc. 5th International Conference on Development and Learning, May 30 - June 3, Bloomgton, IN, 2006. PDF file.

Cognitive mapping engine (later processing for high-dimensional regression):

Other Publications:

DARPA MARS Related Publications

Juyang (John) Weng

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