Spatial and Feature-Based Attention in a Layered Cortical Microcircuit Model

Abstract

Directing attention to the spatial location or the distinguishing feature of a visual object modulates neuronal responses in the visual cortex and the stimulus discriminability of subjects. However, the spatial and feature-based modes of attention differently influence visual processing by changing the tuning properties of neurons. Intriguingly, neurons' tuning curves are modulated similarly across different visual areas under both these modes of attention. Here, we explored the mechanism underlying the effects of these two modes of visual attention on the orientation selectivity of visual cortical neurons. To do this, we developed a layered microcircuit model. This model describes multiple orientation-specific microcircuits sharing their receptive fields and consisting of layers 2/3, 4, 5, and 6. These microcircuits represent a functional grouping of cortical neurons and mutually interact via lateral inhibition and excitatory connections between groups with similar selectivity. The individual microcircuits receive bottom-up visual stimuli and top-down attention in different layers. A crucial assumption of the model is that feature-based attention activates orientation-specific microcircuits for the relevant feature selectively, whereas spatial attention activates all microcircuits homogeneously, irrespective of their orientation selectivity. Consequently, our model simultaneously accounts for the multiplicative scaling of neuronal responses in spatial attention and the additive modulations of orientation tuning curves in feature-based attention, which have been observed widely in various visual cortical areas. Simulations of the model predict contrasting differences between excitatory and inhibitory neurons in the two modes of attentional modulations. Furthermore, the model replicates the modulation of the psychophysical discriminability of visual stimuli in the presence of external noise. Our layered model with a biologically suggested laminar structure describes the basic circuit mechanism underlying the attention-mode specific modulations of neuronal responses and visual perception.

A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing

“…Wagatsuma et al (2013) connected excitatory and inhibitory cells in superficial layers by specifying 4 connection probabilities P EE , P EI , P IE , and P II . We used P EE and P EI for recurrent connections among pyramidal cells and excitatory projections impinging onto the three inhibitory cell types.…”

“…Specifically, we first computed the total number of synapses from inhibitory cells to pyramidal cells used in Wagatsuma et al (2013). Then we split them into three populations using the connection probabilities reported in Pfeffer et al (2013) as weighting factors; see Table 1.…”

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

“…Potjans and Diesmann (2014) have described a large-scale network of multi-layered cortical microcircuits, which was based on the integrated connectivity map derived from anatomical and electrophysiological data. Extending the Potjans' model, Wagatsuma et al (2011, 2013b) explored the intra- and inter-laminar information flow during visual processings and attentional modulation. These works provided cues to help understand how feedforward and feedback signals were integrated within or between layered cortical microcircuits.…”

Visual processing mode switching regulated by VIP cells

“…In the third regime, in which the input to 30 VIP cells is bigger than 1.5 pA, all correlations increase and converge to 1. The most intriguing 31 observation is that the correlations are dissimilar among populations in the second parameter 32 regime, suggesting that VIP cell depolarization can selectively enhance visual responses rather 33 than uniformly. 34…”

“…In conclusion, as cognitive functions may depend on interactions among multiple cortical areas 24 , 31 VIP cells' functional roles could advance our understanding of neural basis of cognitive 32 functions, and we believe that computational models are effective tools to pursue this direction, 33…”