Fig 6. Time scales of learning.

(A) Median gridness scores of the input synaptic weights for 40 random weight initializations and different learning-rate values, i.e., η = (2, 3, 5, 10) ⋅ 10⁻⁵. The weight development is simulated with the detailed spiking model with spatially-regular inputs and constant virtual-rat speed (see also Fig 5). (B) Median gridness scores of the input synaptic weights simulated with constant (black line) and variable (green line) virtual-rat speeds for 40 random weight initializations. Variable running speeds are obtained by sampling from an Ornstein-Uhlenbeck process with long-term mean $\overline{v}=0.25$ m/s, volatility σ_v = 0.1 m ⋅ s^−1.5 and mean-reversion speed θ_v = 10 s⁻¹. The inset shows the distribution of running speeds (mean: 0.25 m/s std: 0.02 m/s). Note that the long-term mean $\overline{v}$ of the process equals the speed v in constant-speed simulations. See Sec Numerical simulations for further details and additional parameter values.