Fig 6. Construction of basis filters using a randomly connected network with feed-forward inhibition via a second population mimicking Golgi cells.

As previously, filters used are τ ₁ = 10ms (blue), τ ₂ = 100ms (green) and τ ₃ = 500ms (red). A,B,C: Goodness of fit (R ²) (A1,B1,C1) and Lyapunov exponents (A2,B2,C2) for three networks with τ _w = 50ms, τ _u = 1ms (A), τ _w = 50ms, τ _u = 50ms (B) and τ _w = 50ms, τ _u = 100ms (C). Results for previous one-population network with τ _w = 50ms shown as light lines. D: Effect of increased sparseness in a two-population network with τ _w = 50ms, τ _u = 1ms: 1. Increase of granule cell population size from Nz = 1k (dark solid lines) to N = 10k (dotted lines). 2. Decrease of convergence to c _u = 10 while keeping network size at Nz = 1k (light solid lines). E: Effect of increased weight variability in a two-population network with τ _w = 50ms, τ _u = 1ms. Compared to control network without weight variability (dark solid lines). 1. Increase of variability for weight of inhibition w (v _w = 4) (dotted lines). 2. Increase of variability for weight of excitation u (v _u = 4) (light lines)