Abstract
We describe a model of invariant visual object recognition in the brain that incorporates different brain areas of the dorsal or ‘where’ and ventral or ‘what’ paths of the visual cortex. The dorsal ‘where’ path is implemented in the model by feedforward and feedback connections between brain areas V1, V2 and a PP module. The ventral ‘what’ path is implemented in a physiologically plausible four-layer network, corresponding to brain areas V1, V2, V4 and IT, with convergence to each part of a layer from a small region of the preceding layer, with feature-based attentional feedback connections, and with local competition between the neurons within a layer implemented by local lateral inhibition. In particular, the model explains the gradually increasing magnitude of the attentional modulation that is found in fMRI experiments from earlier visual areas (V1, V2) to higher ventral visual areas (V4, IT). The model also shows how the effective size of the receptive fields of IT neurons becomes smaller in natural cluttered scenes.
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Deco, G., Rolls, E. (2002). A Neurodynamical Theory of Visual Attention: Comparisons with fMRI- and Single-Neuron Data. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_1
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DOI: https://doi.org/10.1007/3-540-46084-5_1
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