Neural networks combining local excitatory feedback with recurrent inhibition are valuable models of neocortical processing. However, incorporating the attentional modulation observed in cortical neurons is problematic. We propose a simple architecture for attentional processing. Our network consists of two reciprocally connected populations of excitatory neurons; a large population (the map) processes a feedforward sensory input, and a small population (the pointer) modulates location and intensity of this processing in an attentional manner dependent on a control input to the pointer. This pointer-map network has rich dynamics despite its simple architecture and explains general computational features related to attention/intention observed in neocortex, making it interesting both theoretically and experimentally.