This work presents a new architecture of artificial neural networks - Venn Networks, which produce localized activations in a 2D map while executing simple cognitive tasks. These activations resemble the ones observed in patches of the cerebral cortex when inspected by functional imaging methods such as fMRI. Venn-networks allow simultaneous incorporation of four distinct and independent concepts, all present in biological neural network. These concepts are (a) cyto-architectonic regions, (b) localization of functional activations, (c) complex pattern of intra/interregional connectivity, and (d) definable damages to the neurons and axons. The dynamics of Venn-networks is highly influenced by these concepts. The proposed architecture incorporates both unsupervised and supervised learning paradigms; it also implements open and closed loops that can be assembled with afferent, efferent and U-fiber type of connections. Venn-networks were devised to integrate in one single model the topographical representation of neural activations and also functional results evoked by these activations. Following the description of the architecture and its components, we present some simulation results that implement above-mentioned concepts (a), (b) and (c). In those simulations, virtual fingers are controlled by Venn-networks similarly to the sensorimotor feedback that controls fine movements of fingers in the CNS. The trained Venn-networks emulate the finger movements of a piano player performing The Sonata Facile of Mozart. © 2006 IEEE.