Abstract
Visuospatial attention produces myriad effects on the activity and selectivity of cortical neurons. Spiking neuron models capable of reproducing a wide variety of these effects remain elusive. We present a model called the Attentional Routing Circuit (ARC) that provides a mechanistic description of selective attentional processing in cortex. The model is described mathematically and implemented at the level of individual spiking neurons, with the computations for performing selective attentional processing being mapped to specific neuron types and laminar circuitry. The model is used to simulate three studies of attention in macaque, and is shown to quantitatively match several observed forms of attentional modulation. Specifically, ARC demonstrates that with shifts of spatial attention, neurons may exhibit shifting and shrinking of receptive fields; increases in responses without changes in selectivity for non-spatial features (i.e. response gain), and; that the effect on contrast-response functions is better explained as a response-gain effect than as contrast-gain. Unlike past models, ARC embodies a single mechanism that unifies the above forms of attentional modulation, is consistent with a wide array of available data, and makes several specific and quantifiable predictions.
Highlights
Several decades of physiology, imaging and psychophysics research on attention have generated an enormous amount of data describing myriad forms of attentional influence [1,2,3,4,5]
Attentional Effects on Receptive Field Profiles Womelsdorf et al [3] measured receptive field (RF) profiles in macaque area MT while two random dot pattern (RDP) stimuli were presented in the RF (S1 and S2), and one RDP stimulus was outside the RF (S3)
We described the Attentional Routing Circuit (ARC) in the context of the ventral stream to indicate its application to a multi-level hierarchy, and here we apply it to a single level in the dorsal stream to address the available empirical evidence
Summary
Several decades of physiology, imaging and psychophysics research on attention have generated an enormous amount of data describing myriad forms of attentional influence [1,2,3,4,5]. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail [3,6,7,8,9,10]. Models that simultaneously are able to reproduce attentional effects from multiple studies, and that provide a neurally detailed mechanism are rare. There remains a need for neurally detailed mechanistic models, especially those which provide experimentally testable predictions. We describe a functional mechanism for attentional routing in a large-scale hierarchical model, and demonstrate the biological plausibility of the model by presenting a spiking neuron implementation that accounts for five forms of attentional effect. We demonstrate that the model exhibits shrinking and shifting, but not amplitude changes of spatial receptive field (RF) profiles; an increase of tuning curve gain without sharpening; and response gain effects in neuronal contrast-response functions
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