Linking Patterns of Infant Eye Movements to a Neural Network Model of the Ventral Stream Using Representational Similarity Analysis

2021 ◽  
Author(s):  
John E. Kiat ◽  
Steven J. Luck ◽  
Aaron G. Beckner ◽  
Taylor R. Hayes ◽  
Katherine I. Pomaranski ◽  
...  
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Network Model ◽  
Neural Network Model ◽  
Ventral Stream ◽  
Similarity Analysis ◽  
Representational Similarity Analysis

Microstimulation of a Neural-Network Model for Visually Guided Saccades

1989 ◽  
Vol 1 (4) ◽  
pp. 317-326 ◽  
Author(s):  
Sabrina J. Goodman ◽  
Richard A. Andersen
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Network Model ◽  
Neural Network Model ◽  
Posterior Parietal Cortex ◽  
Single Cells ◽  
Middle Layer ◽  
Retinal Position ◽  
Area 7A ◽  
The Brain

Microstimulation of many saccadic centers in the brain produces eye movements that are not consistent with either a strictly retinal or strictly head-centered coordinate coding of eye movements. Rather, stimulation produces some features of both types of coordinate coding. Recently we demonstrated a neural network model that was trained to localize the position of visual stimuli in head-centered coordinates at the output using inputs of eye and retinal position similar to those converging on area 7a of the posterior parietal cortex of monkeys (Zipser & Andersen 1988; Andersen & Zipser 1988). Here we show that microstimulation of this trained network, achieved by fully activating single units in the middle layer, produces “saccades” that are very much like the saccades produced by stimulating the brain. The activity of the middle-layer units can be considered to code the desired location of the eyes in head-centered coordinates; however, stimulation of these units does not produce the saccades predicted by a classical head-centered coordinate coding because the location in space appears to be coded in a distributed fashion among a population of units rather than explicitly at the level of single cells.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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