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\begin{table}[h]
\caption[]{{\bf Comparison of the neurogenesis model and the random initialization model for different input dimensionalities.} The simplified model with $s = 0.8$ (similar input clusters) is used. Pretraining with two clusters and subsequent learning of a novel cluster 3 (Neuro.) was performed in the same way as reported in the main text. After full maturation of the newborn DGC (2 epochs), its weights were fixed, and patterns of a novel cluster 4 were introduced as well as another newborn DGC, and so on until all seven clusters were learned. Reconstruction errors were computed at the end of learning of all seven clusters, and compared with two cases where newborn DGCs do not undergo a two-phase maturation during their 2 epochs of learning, always stay plastic, and are born with a randomly initialized  feedforward weight vector: one where the L2-norm of the weight vector starts at a low value of 0.1 (RandInitL.), and one where the L2-norm starts at 1.5, which is the upper bound for the length of the weight vector (RandInitH.). We compare the reconstruction error between the neurogenesis model and the random initialization models for different values of the effective input dimensionality (PR), which depends on the concentration parameter ($\kappa$) used when creating the artificial dataset. The results with the dataset used in the main text ($\kappa = 10^4$, PR $=11$) are reported here for comparison. }
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$\kappa$ & $10^4$ & $10^3$ & $8 \cdot 10^2$ & $7 \cdot 10^2$ & $6 \cdot 10^2$  \\
PR & 11 & 72 & 83 & 90 & 97  \\
Neuro. & 0.013 & 0.122 & 0.150 & 0.170 & 0.377  \\
RandInitL. & 0.131 & 0.168 & 0.197 & 0.375 & 0.721  \\
RandInitH. & 0.182 & 0.154 & 0.158 & 0.170 & 0.376 \\

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