Biomaterial Database

Effect of binocular cortical misalignment on ocular dominance and orientation selectivity. 1996

H Shouval, and N Intrator, and C C Law, and L N Cooper

Department of Physics and Neuroscience, Brown University, Providence, RI 02912, USA.

We model a two-eye visual environment composed of natural images and study its effect on single cell synaptic modification. In particular, we study the effect of binocular cortical misalignment on receptive field formation after eye opening. We show that binocular misalignment affects principal component analysis (PCA) and Bienenstock, Cooper, and Munro (BCM) learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case.

UI	MeSH Term	Description	Entries
D007858	Learning	Relatively permanent change in behavior that is the result of past experience or practice. The concept includes the acquisition of knowledge.	Phenomenography
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
D009949	Orientation	Awareness of oneself in relation to time, place and person.	Cognitive Orientation,Mental Orientation,Psychological Orientation,Cognitive Orientations,Mental Orientations,Orientation, Cognitive,Orientation, Mental,Orientation, Psychological,Orientations,Orientations, Cognitive,Orientations, Mental,Orientations, Psychological,Psychological Orientations
D014794	Visual Fields	The total area or space visible in a person's peripheral vision with the eye looking straightforward.	Field, Visual,Fields, Visual,Visual Field
D015348	Vision, Binocular	The blending of separate images seen by each eye into one composite image.	Binocular Vision
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