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Feedback interactions between neuronal pointers and maps for attentional processing.

R Hahnloser·R J Douglas·M Mahowald·K Hepp
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Abstract

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.

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AttentionBrain MappingFeedbackMental ProcessesModels, NeurologicalNeocortexNerve NetNeuronsSynapsesJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

R Hahnloser, R J Douglas, M Mahowald, K Hepp. 1999. Feedback interactions between neuronal pointers and maps for attentional processing.. https://doi.org/10.1038/11219

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