Attention and Optimal Sensory Codes

Neuronal activity can be modulated by attention even while the sensory stimulus is held fixed. This modulation implies changes in the tuning curve (or receptive field) of the neurons involved in sensory processing. We propose an information-theoretic hypothesis for the purpose of this modulation, and show using computer simulation that the similar modulation emerges in a system that is optimally encoding a sensory stimulus when the system is informed about the changing relevance of different features of the input. We present a simple model that learns a covert attention mechanism, given input patterns and tradeoff requirements. After optimization, the system gains the ability to reorganize its computational resources (or coding strategy) depending on the incoming covert attentional signal, using only threshold shifts in neurons throughout the network. The modulation of activity of the encoding units for different attentional states qualitatively matches that observed in animal selective attention experiments. Due to its generality, the model can be applied to any modality, and to any attentional goal