Feedback interactions between neuronal pointers and maps for attentional processing

Neural networks combining local excitatory feedback with recurrent inhibiti on are valuable models of neocortical processing. However, incorporating th e attentional modulation observed in cortical neurons is problematic. We pr opose a simple architecture for attentional processing. Our network consist s of two reciprocally connected populations of excitatory neurons; a large population (the map) processes a feedforward sensory input, and a small pop ulation (the pointer) modulates location and intensity of this processing i n an attentional manner dependent on a control input to the pointer. This p ointer-map network has rich dynamics despite its simple architecture and ex plains general computational features related to attention/intention observ ed in neocortex, making it interesting both theoretically and experimentall y.