What is the goal of sensory coding?
Neural Computation
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transform-invariant recognition by association in a recurrent network
Neural Computation
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Object Classification Using a Fragment-Based Representation
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Journal of Cognitive Neuroscience
Detailed Exploration of Face-related Processing in Congenital Prosopagnosia: 1. Behavioral Findings
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Portraits or People? Distinct Representations of Face Identity in the Human Visual Cortex
Journal of Cognitive Neuroscience
Dissociations of Face and Object Recognition in Developmental Prosopagnosia
Journal of Cognitive Neuroscience
Face-selective Activation in a Congenital Prosopagnosic Subject
Journal of Cognitive Neuroscience
The Fusiform "Face Area" is Part of a Network that Processes Faces at the Individual Level
Journal of Cognitive Neuroscience
Information maximization in face processing
Neurocomputing
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Congenital prosopagnosia is a selective deficit in face identification that is present from birth. Previously, behavioral deficits in face recognition and differences in the neuroanatomical structure and functional activation of face processing areas have been documented mostly in separate studies. Here, we propose a neural network model of congenital prosopagnosia which relates behavioral and neuropsychological studies of prosopagnosia to theoretical models of information processing. In this study we trained a neural network with two different algorithms to represent face images. First, we introduced a predisposition towards a decreased network connectivity implemented as a temporal independent component analysis (ICA). This predisposition induced a featural representation of faces in terms of isolated face parts. Second, we trained the network for optimal information encoding using spatial ICA, which led to holistic representations of faces. The network model was then tested empirically in an experiment with ten prosopagnosic and twenty age-matched controls. Participants had to discriminate between faces that were changed either according to the prosopagnosic model of featural representation or to the control model of holistic representation. Compared to controls prosopagnosic participants were impaired only in discriminating holistic changes of faces but showed no impairment in detecting featural changes. In summary, the proposed model presents an empirically testable account of congenital prosopagnosia that links the critical features - a lack of holistic processing at the computational level and a sparse structural connectivity at the implementation level. More generally, our results point to structural differences in the network connectivity as the cause of the face processing deficit in congenital prosopagnosia.