Biomaterial Database

Autonomous development of vergence control driven by disparity energy neuron populations. 2010

Yiwen Wang, and Bertram E Shi

Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong. eewangyw@ust.hk

We present a simple optimization criterion that leads to autonomous development of a sensorimotor feedback loop driven by the neural representation of the depth in the mammalian visual cortex. Our test bed is an active stereo vision system where the vergence angle between the two eyes is controlled by the output of a population of disparity-selective neurons. By finding a policy that maximizes the total response across the neuron population, the system eventually tracks a target as it moves in depth. We characterized the tracking performance of the resulting policy using objects moving both sinusoidally and randomly in depth. Surprisingly, the system can even learn how to track based on stimuli it cannot track: even though the closed loop 3 dB tracking bandwidth of the system is 0.3 Hz, correct tracking policies are learned for input stimuli moving as fast as 0.75 Hz.

UI	MeSH Term	Description	Entries
D009039	Motion Perception	The real or apparent movement of objects through the visual field.	Movement Perception,Perception, Motion,Perception, Movement
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D010775	Photic Stimulation	Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.	Stimulation, Photic,Visual Stimulation,Photic Stimulations,Stimulation, Visual,Stimulations, Photic,Stimulations, Visual,Visual Stimulations
D011336	Probability	The study of chance processes or the relative frequency characterizing a chance process.	Probabilities
D012054	Reinforcement, Psychology	The strengthening of a conditioned response.	Negative Reinforcement,Positive Reinforcement,Psychological Reinforcement,Reinforcement (Psychology),Negative Reinforcements,Positive Reinforcements,Psychological Reinforcements,Psychology Reinforcement,Psychology Reinforcements,Reinforcement, Negative,Reinforcement, Positive,Reinforcement, Psychological,Reinforcements (Psychology),Reinforcements, Negative,Reinforcements, Positive,Reinforcements, Psychological,Reinforcements, Psychology
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D012371	Robotics	The application of electronic, computerized control systems to mechanical devices designed to perform human functions. Formerly restricted to industry, but nowadays applied to artificial organs controlled by bionic (bioelectronic) devices, like automated insulin pumps and other prostheses.	Companion Robots,Humanoid Robots,Remote Operations (Robotics),Social Robots,Socially Assistive Robots,Telerobotics,Soft Robotics,Assistive Robot, Socially,Companion Robot,Humanoid Robot,Operation, Remote (Robotics),Operations, Remote (Robotics),Remote Operation (Robotics),Robot, Companion,Robot, Humanoid,Robot, Social,Robot, Socially Assistive,Robotic, Soft,Social Robot,Socially Assistive Robot,Soft Robotic
D014793	Visual Cortex	Area of the OCCIPITAL LOBE concerned with the processing of visual information relayed via VISUAL PATHWAYS.	Area V2,Area V3,Area V4,Area V5,Associative Visual Cortex,Brodmann Area 18,Brodmann Area 19,Brodmann's Area 18,Brodmann's Area 19,Cortical Area V2,Cortical Area V3,Cortical Area V4,Cortical Area V5,Secondary Visual Cortex,Visual Cortex Secondary,Visual Cortex V2,Visual Cortex V3,Visual Cortex V3, V4, V5,Visual Cortex V4,Visual Cortex V5,Visual Cortex, Associative,Visual Motion Area,Extrastriate Cortex,Area 18, Brodmann,Area 18, Brodmann's,Area 19, Brodmann,Area 19, Brodmann's,Area V2, Cortical,Area V3, Cortical,Area V4, Cortical,Area V5, Cortical,Area, Visual Motion,Associative Visual Cortices,Brodmanns Area 18,Brodmanns Area 19,Cortex Secondary, Visual,Cortex V2, Visual,Cortex V3, Visual,Cortex, Associative Visual,Cortex, Extrastriate,Cortex, Secondary Visual,Cortex, Visual,Cortical Area V3s,Extrastriate Cortices,Secondary Visual Cortices,V3, Cortical Area,V3, Visual Cortex,V4, Area,V4, Cortical Area,V5, Area,V5, Cortical Area,V5, Visual Cortex,Visual Cortex Secondaries,Visual Cortex, Secondary,Visual Motion Areas
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron