multiplicative normalization
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2014 ◽  
Author(s):  
Stephanie B Stewart ◽  
Jonathan M Koller ◽  
Meghan C Campbell ◽  
Joel S Perlmutter ◽  
Kevin J Black

To determine how different methods of normalizing for global cerebral blood flow (gCBF) affect image quality and sensitivity to cortical activation, pulsed arterial spin labeling (pASL) scans obtained during a visual task were normalized by either additive or multiplicative normalization of modal gCBF. Normalization by either method increased the statistical significance of cortical activation by a visual stimulus. However, image quality was superior with additive normalization, whether judged by intensity histograms or by reduced variability within gray and white matter.


2014 ◽  
Author(s):  
Stephanie B Stewart ◽  
Jonathan M Koller ◽  
Meghan C Campbell ◽  
Joel S Perlmutter ◽  
Kevin J Black

To determine how different methods of normalizing for global cerebral blood flow (gCBF) affect image quality and sensitivity to cortical activation, pulsed arterial spin labeling (pASL) scans obtained during a visual task were normalized by either additive or multiplicative normalization of modal gCBF. Normalization by either method increased the statistical significance of cortical activation by a visual stimulus. However, image quality was superior with additive normalization, whether judged by intensity histograms or by reduced variability within gray and white matter.


2014 ◽  
Author(s):  
Stephanie B Stewart ◽  
Jonathan M Koller ◽  
Meghan C Campbell ◽  
Joel S Perlmutter ◽  
Kevin J Black

To determine how different methods of normalizing for global cerebral blood flow (gCBF) affect image quality and sensitivity to cortical activation, pulsed arterial spin labeling (pASL) scans obtained during a visual task were normalized by either additive or multiplicative normalization of modal gCBF. Normalization by either method increased the statistical significance of cortical activation by a visual stimulus. However, image quality was superior with additive normalization by visual inspection, by comparing intensity histograms, and by reduction of the variability within gray and white matter.


2014 ◽  
Author(s):  
Stephanie B Stewart ◽  
Jonathan M Koller ◽  
Meghan C Campbell ◽  
Joel S Perlmutter ◽  
Kevin J Black

To determine how different methods of normalizing for global cerebral blood flow (gCBF) affect image quality and sensitivity to cortical activation, pulsed arterial spin labeling (pASL) scans obtained during a visual task were normalized by either additive or multiplicative normalization of modal gCBF. Normalization by either method increased the statistical significance of cortical activation by a visual stimulus. However, image quality was superior with additive normalization by visual inspection, by comparing intensity histograms, and by reduction of the variability within gray and white matter.


2010 ◽  
Vol 2010 (5) ◽  
Author(s):  
Richard D. Ball ◽  
◽  
Luigi Del Debbio ◽  
Stefano Forte ◽  
Alberto Guffanti ◽  
...  

2003 ◽  
Vol 15 (4) ◽  
pp. 937-963 ◽  
Author(s):  
Terry Elliott

In standard Hebbian models of developmental synaptic plasticity, synaptic normalization must be introduced in order to constrain synaptic growth and ensure the presence of activity-dependent, competitive dynamics. In such models, multiplicative normalization cannot segregate afferents whose patterns of electrical activity are positively correlated, while subtractive normalization can. It is now widely believed that multiplicative normalization cannot segregate positively correlated afferents in any Hebbian model. However, we recently provided a counterexample to this belief by demonstrating that our own neurotrophic model of synaptic plasticity, which can segregate positively correlated afferents, can be reformulated as a nonlinear Hebbian model with competition implemented through multiplicative normalization. We now perform an analysis of a general class of Hebbian models under general forms of synaptic normalization. In particular, we extract conditions on the forms of these rules that guarantee that such models possess a fixed-point structure permitting the segregation of all but perfectly correlated afferents. We find that the failure of multiplicative normalization to segregate positively correlated afferents in a standard Hebbian model is quite atypical.


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