The attentional routing circuit: receptive field modulation through nonlinear dendritic interactions

Cognitive and Systems Neuroscience, 2011

Bruce Bobier, Terrence C. Stewart, Chris Eliasmith


We present a model of attentional routing called the Attentional Routing Circuit (ARC) that extends an existing model of spiking neurons with dendritic nonlinearities. Specifically, we employ the Poirazi et al. (2003) pyramidal neuron in a population coding framework. ARC demonstrates that the dendritic nonlinearities can be exploited to result in selective routing, with a decrease in the number of cells needed by a factor of ~5 as compared with a linear dendrite model. Routing of attended information occurs through the modulation of feedforward visual signals by a cortical control signal specifying the location and size of the attended target. The model is fully specified in spiking single cells. Our approach differs from past work on shifter circuits by having more efficient control, and using a more biologically detailed substrate. Our approach differs from existing models that use gain fields by providing precise hypotheses about how the control signals are generated and distributed in a hierarchical model in spiking neurons. Further, the model accounts for numerous experimental findings regarding the timing, strength and extent of attentional modulation in ventral stream areas, and the perceived contrast enhancement of attended stimuli. To further demonstrate the plausibility of ARC, it is applied to the attention experiments of Womelsdorf et al. (2008) and tested in detail. For the simulations, the model has only two free parameters that influence its ability to match the experimental data, and without fitting, we show that it can account for the experimental observations of changes in receptive field (RF) gain and position with attention in macaques. In sum, the model provides an explanation of RF modulation as well as testable predictions about nonlinear cortical dendrites and attentional changes of receptive field properties.

Full text links


Conference Proceedings

Cognitive and Systems Neuroscience
Salt Lake City, UT


Plain text