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.

[1]  Gustavo Deco,et al.  A Neurodynamical Model of Visual Attention: Feedback Enhancement of Spatial Resolution in a Hierarchical System , 2001, Journal of Computational Neuroscience.

[2]  G. Humphreys,et al.  Attention, spatial representation, and visual neglect: simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). , 2003, Psychological review.

[3]  J. Maunsell,et al.  Effects of Attention on the Processing of Motion in Macaque Middle Temporal and Medial Superior Temporal Visual Cortical Areas , 1999, The Journal of Neuroscience.

[4]  PIERRE VAN DE LAAR,et al.  Task-Dependent Learning of Attention , 1997, Neural Networks.

[5]  R. Desimone,et al.  Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. , 1997, Journal of neurophysiology.

[6]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[7]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[8]  D. V. van Essen,et al.  A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[10]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[11]  Geoffrey E. Hinton,et al.  Shape Recognition and Illusory Conjunctions , 1985, IJCAI.

[12]  P Kuyper,et al.  Triggered correlation. , 1968, IEEE transactions on bio-medical engineering.