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Outline

Title
Abstract
Introduction
Implication of Genomic Equivalence
Layer Scale: Lobe Component Analysis
Experimental Examples
Multiple Objects in Complex Background
Conclusions and Discussion
References
All Topics
Computer Science
Computer Vision

Brain-Like Emergent Spatial Processing

Profile image of Matthew LuciwMatthew Luciw

2011

Abstract

This is a theoretical, modeling, and algorithmic paper about the spatial aspect of brain-like information processing, modeled by the Developmental Network (DN) model. The new brain architecture allows the external environment (including teachers) to interact with the sensory ends S and the motor ends M of the skull-closed brain B through development. It does not allow the human programmer to hand-pick extra-body concepts or to handcraft the concept boundaries inside the brain B. Mathematically, the brain spatial processing performs real-time mapping from S(t)×B(t)×M (t) to S(t+1)×B(t+1)×M (t+1), through network updates, where the contents of S, B, M all emerge from experience. Using its limited resource, the brain does increasingly better through experience. A new principle is that the effector ends in M serve as hubs for concept learning and abstraction. The effector ends B serve also as input and the sensory ends S serve also as output. As DN embodiments, the Where-What Networks (WWNs) present three major function novelties -new concept abstraction, concept as emergent goals, and goal-directed perception. The WWN series appears to be the first general purpose emergent systems for detecting and recognizing multiple objects in complex backgrounds. Among others, the most significant new mechanism is general-purpose top-down attention.

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  108. Juyang Weng (S85-M88-SM05-F09) received the BS degree in computer science from Fudan University, Shanghai, China, in 1982, and M. Sc. and PhD degrees in computer science from the University of Illinois at Urbana- Champaign,in 1985 and 1989, respectively. He is currently a professor of Computer Science and Engineering at Michigan State University, East Lansing. He is also a faculty member of the Cognitive Science Program and the Neuroscience Program at Michigan State University. Since the work of Cresceptron (ICCV 1993), he expanded his research interests in biologically inspired systems, especially the au- tonomous development of a variety of mental capabilities by robots and animals, including perception, cognition, behaviors, motivation, and abstract reasoning skills. He has published over 250 research articles on related subjects, including task muddiness, intelligence metrics, mental architectures, vision, audition, touch, attention, recognition, autonomous navigation, natural language understanding, and other emergent behaviors. Dr. Weng is an Editor-in-Chief of International Journal of Humanoid Robotics and an associate editor of the IEEE Transactions on Autonomous Mental Development. He was a Program Chairman of the NSF/DARPA funded Workshop on Development and Learning 2000 (1st ICDL), a Program Chairman of the 2nd ICDL (2002), the chairman of the Autonomous Mental Development Technical Committee of the IEEE Computational Intelligence Society (2004-2005), the Chairman of the Governing Board of the Interna- tional Conferences on Development and Learning (ICDLs) (2005-2007), a General Chairman of 7th ICDL (2008), the General Chairman of 8th ICDL (2009), an associate editor of IEEE Transactions on Pattern Recognition and Machine Intelligence, and an associate editor of IEEE Transactions on Image Processing. Matthew Luciw received the M.S. and Ph.D. degrees in computer science from Michigan State University (MSU), East Lansing, in 2006 and 2010, respectively. He was previously a member of the Embodied Intelligence Laboratory at MSU. He is currently working as a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, Switzerland. His research involves the study of biologically-inspired algorithms to enable autonomous learning agents. He is a member of the IEEE Computational Intelligence Society.

Related papers

Bio-inspired Model of Spatial Cognition
Michal Vavrecka

Lecture Notes in Computer Science, 2011

We present the results of an ongoing research in the area of symbol grounding. We develop a biologically inspired model for grounding the spatial terms that employs separate visual what and where subsystems that are integrated with the symbolic linguistic subsystem in the simplified neural model. The model grounds color, shape and spatial relations of two objects in 2D space. The images with two objects are presented to an artificial retina and five-word sentences describing them (e.g. "Red box above green circle") with phonological encoding serve as auditory inputs. The integrating multimodal module is implemented by Self-Organizing Map or Neural Gas algorithms in the second layer. We found out that using NG leads to better performance especially in case of the scenes with higher complexity, and current simulations also reveal that splitting the visual information and simplifying the objects to rectangular monochromatic boxes facilitates the performance of the where system and hence the overall functionality of the model.

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From Neural Networks to the Brain: Autonomous Mental Development
Juyang Weng

Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces recent results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: visionguided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, four tasks which infants learn to perform early but very perceptually challenging for robots.

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A Biologically Inspired Spatial Computer That Learns to See and Act
Paul Robertson

Vision and motor control are usually studied as separate phenomenon. They perform very different functions, they are performed by different regions of the brain, and one is perception while the other is actuation. The two structures did, however, co-evolve. While they are different structures they work together in reasoning about and manipulating the outside world. Both structures have some similar attributes. For example both the motor cortex and the visual cortex are laid out in a manner that preserves topological adjacency and the hippocampus, where positional awareness is represented also represents places in the world through a topological map. In all case the layout of the areas suggests algorithms that depend upon propagation through a kind of spatial computer in order to solve navigational tasks that combine perception, actuation, and spatial awareness. In this paper we take the position that it makes sense to study the computational aspects of learning to perform such tasks...

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Brain Model For Developmental Robots : The Spatial Brain for Any Temporal Lengths
Juyang Weng

2010

I present a general purpose model of the brain, called Self-Aware and Self-Effecting (SASE) model. Rooted in the biological genomic equivalence principle, our model proposes a general-purpose cell-centered in-place learning scheme to handle all levels of brain development and operation, from the cell level all the way to the brain level. It clarifies five necessary “chunks” of the brain “puzzle”: development, architecture, area, space and time. Then, I concentrate on how such a model enables a developmental robot to deal with temporal contexts. It deals with temporal context of any length without a dedicated temporal component.

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Skull-closed autonomous development: WWN-6 using natural video
Juyang Weng

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012

While a physical environment interacts with a human individual through the brain's sensors and effectors, internal representations inside the skull-closed brain autonomously emerge and adapt throughout the lifetime. By "skull-closed", we mean that the brain inside the skull is off limit to all teachers in the external physical environment, except the brain's sensory ends and motor ends. We present the Where-What Network 6 (WWN-6), which has realized our goal of fully autonomous development inside a closed network "skull". This means that the human programmer is not allowed to handcraft the internal representation for any fixed extra-body concepts. For example, the meanings of specific values of location or type concept are not known during the programming time. Such meanings emerge through associations imbedded in 'postnatal" experience. This capability is especially challenging when one considers the fact that most elements in the sensory ends are irrelevant to the signals at the effector ends (e.g., many background pixels). How does each vector value in the effector find its corresponding pattern in the correct patch of the sensory image? We outline this autonomous learning theory for the brain and present how the developmental program (DP) of WWN-6 enables the network to perform for attending and recognizing objects in complex backgrounds using natural video. The inputs to the agent (i.e., the network) are not artificially synthesized images as WWNs used before, but drawn from continuous video taken from natural settings where, in general, everything is moving.

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