comparable magnitudes of the stimulus strength and threshold, | c|∼|T|, we see a transition in which Talso begins to sharpen the tuning curve and continues to do so until the threshold surpasses h(θ,t)f o ra l l θ(i. e., for δ⋆ 1=δ⋆ 2= 0). Accordingly, for higher stimulus strengths, the thresholding nonlinearity plays a greater role in modulating the network selectivity at lower and a wider range of Tvalues. Roles of the cortical weights. The anisotropic connectivity strength J1exhibits similar relation-ships to the tuning curve properties to those of c. That is, for T<0, a∞(θ/C22) grows and Δcnarrows with increasing J1(see Figure 9A ). The trend with respect to Δcreverses for T>0( Figure 9B ), whereas the trend with respect to a∞(θ/C22) remains unaffected. These similarities are a mark of the competition between the external input and the cortical parameters in driving the network selectivity and reflect the fact that both parameters modulate the anisotropic terms of the model. This means that the role of J1in driving network selectivity becomes more significant with decreasing stimulus strength (see Figure 10 ). However, a large external input does not suppress the contribution of J1to the overall network activity. That is, increasing J1results in raising a∞(θ/C22), regardless of the strength of the stimulus. Similarly, a Figure 9. Effect of J1on the tuning curve properties for varying values of T. β=1, c= 0. 3, J0=-10, and θ/C22=0. ( A ) T=-1. (B) T= 0. 2. Figure 10. Effect of J1on the tuning curve properties for different stimulus strengths. β=1, T=-5,J0=-9, and θ/C22=0. ( A ) c=1. ( B ) c=3. Network Neuroscience 693Pattern forming mechanisms of color vision Downloaded from http://direct. mit. edu/netn/article-pdf/7/2/679/2118501/netn_a_00294. pdf by guest on 09 August 2024 