Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Representation of similarity in three-dimensional object discrimination
Neural Computation
Similarity, connectionism, and the problem of representation in vision
Neural Computation
Psychophysically inspired bayesian occlusion model to recognize occluded faces
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Recognizing occluded faces by exploiting psychophysically inspired similarity maps
Pattern Recognition Letters
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This paper presents a neural model of similarity perception in identification tasks. It is based on self-organizing maps and population coding and is examined through five different identification experiments. Simulating an identification task, the neural model generates a confusion matrix that can be compared directly with that of human subjects. The model achieves a fairly accurate match with the pertaining experimental data both during training and thereafter. To achieve this fit, we find that the entire activity in the network should decline while learning the identification task, and that the population encoding of the specific stimuli should become sparse as the network organizes. Our results, thus, suggest that a self-organizing neural model employing population coding can account for identification processing while suggesting computational constraints on the underlying cortical networks.