Abstract not found

Aneuralnetworkmodelforobjectrecognitionin cluttered scenes using motion and binocular

We present a recurrent multimodal model of binding written words to mental objects or concepts and investigate the capability of the network in reading misspelt but categorically related words. Our model consists of three mutually interconnected association modules which store mental objects, represent their written names and bind these together to form mental concepts. A controllable feedback gain term controlling top-down influence is incorporated into the model architecture and it is shown that correct settings for this during map formation and simulated reading experiments is necessary for correct interpretation and semantic binding of the written words. 1