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. 2016 Nov 7;6:35831. doi: 10.1038/srep35831

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Copyright © 2016, The Author(s)

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Simulations of a network of GL neurons with fixed W_ij = W = 1, u = 1, A = 1.1 and τ = 1000 ms. Dynamic gains Γ_i[t] starts with Γ_i[0] uniformly distributed in [0, Γ_max]. The average initial condition is , which produces the different initial conditions Γ[0]. (a) Self-organization of the average gain Γ[t] over time. The horizontal dashed line marks the value Γ_C = 1. (b) Data collapse for C_S(s)s^1/2 versus for several N, with the cutoff exponent c_S = 1.

Inline graphic — Simulations of a network of GL neurons with fixed W_ij = W = 1, u = 1, A = 1.1 and τ = 1000 ms. Dynamic gains Γ_i[t] starts with Γ_i[0] uniformly distributed in [0, Γ_max]. The average initial condition is , which produces the different initial conditions Γ[0]. (a) Self-organization of the average gain Γ[t] over time. The horizontal dashed line marks the value Γ_C = 1. (b) Data collapse for C_S(s)s^1/2 versus for several N, with the cutoff exponent c_S = 1.