Intrinsic Organization of Contrast Sensitivity in Human Vision
Natural vision operates over an enormous range of luminance while preserving sensitivity to both fine and coarse spatial structure. As luminance increases, the peak of the contrast sensitivity function shifts toward higher spatial frequencies. Existing accounts explain how luminance rescales the amplitude of neural responses through gain control, but not why preferred spatial frequency should…
The human visual system can perceive contrast sensitivity across an extensive range of luminance levels, while still maintaining sensitivity to both fine and coarse spatial details. As the overall light intensity increases, the contrast sensitivity function's peak tends to move towards higher spatial frequencies. While current theories suggest that the brain adjusts the strength of neural responses to match varying light levels, they do not explain why the preferred spatial frequency should change systematically with luminance.
In this study, the researchers propose that recurrent excitatory-inhibitory interactions in the neural circuit generate an intrinsic spatial frequency, determined by the circuit's connectivity, which influences the network's resonance properties and, consequently, predicts the shift in preferred spatial frequency. When the mean luminance changes, this intrinsic spatial frequency adjusts by altering the balance between excitatory and inhibitory signals, thereby transitioning the network between different dynamical states instead of merely scaling its responses.
To validate this hypothesis, the researchers measured human contrast sensitivity over a wide range of luminance levels. The findings showed that the preferred spatial frequency remained relatively constant at low light levels, but then experienced a sharp increase over a narrow range in an S-shaped transition. These observations support the idea that the organization of cortical circuits changes in response to luminance-dependent perception, thereby linking luminance to the dynamics of cortical computation.
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