At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters
Fig 3
Construction of basis filters using a randomly connected network with feed-forward inhibition using LASSO regression.
A1,B1,C1: Lyapunov exponent of randomly connected networks with increasing weight w. Networks with three different feed-forward inhibition time-constants are considered: τw = 10ms (A1), τw = 50ms (A2) and τw = 100ms (A3). Vertical black lines visualize the “edge-of-chaos” as defined in Methods. A2,B2,C2: Goodness of fit (R2) of three exponential filters (τ1 = 10ms (blue), τ2 = 100ms (green) and τ3 = 500ms (red)) constructed from responses of corresponding networks above.