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A
R-P
learning applied to a network model of cortical area 7a
Pietro Mazzoni
, Richard A. Andersen
, Michael I. Jordan
Institute of Clinical and Translational Sciences (ICTS)
Section of Movement Disorders
Research output
:
Contribution to conference
›
Paper
›
peer-review
3
Scopus citations
Overview
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R-P
learning applied to a network model of cortical area 7a'. Together they form a unique fingerprint.
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Computer Science
Neural Network
100%
Reference Frame
100%
Cortical Area
100%
Network Architecture
100%
Supervised Learning
100%
Gradient Descent
100%
Visual Stimuli
100%
Connection Strength
100%
Learning Network
100%
Layered Architecture
100%
Receptive Field
100%
Keyphrases
Cortical Areas
100%
Network Model
100%
Area 7a
100%
Eye Position
50%
Response Properties
50%
Backpropagation
50%
Neural Network
25%
Visual Response
25%
Biologically Plausible
25%
Visual Stimuli
25%
Primate Cortex
25%
Similarity Transformation
25%
Network Architecture
25%
Connection Strength
25%
Unique Response
25%
Receptive Field
25%
Hidden Unit
25%
Gradient Method
25%
Learning Paradigm
25%
Supervised Learning
25%
Retinotopic
25%
Learning Networks
25%
Reinforcement Signal
25%
Learning Rule
25%
Back Propagation Algorithm
25%
Layered Architecture
25%
Psychology
Network Model
100%
Receptive Field
100%
Neural Network
100%
Neuroscience
Neural Network
100%
Receptive Field
100%