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\begin{table}[h]
\caption[]{{\bf Classification performance with plastic mature DGCs.} Top: Using the main neurogenesis network with $N_{DGC}=100$  DGCs, we keep the learning rate of newborn DGCs at $\eta = 0.01$, but now set the learning rate of mature DGCs  to nonzero values ($\eta_{\text{mature}}>0$) throughout maturation of newborn DGCs. This enables us to vary the level of  remaining plasticity in mature DGCs. The number of newborn DGCs that undergo neurogenesis ($N_{\text{newborn}}=21$) is the same as in the main text. Overall classification performance for digits 3, 4, and 5 ($P$) is computed at the end of the late phase of maturation of newborn DGCs, as well as the classification performance for digit 3 ($P_3$), digit 4 ($P_4$) and digit 5 ($P_5$).  Bottom: Same with the extended neurogenesis network with $N_{DGC}=700$. The number of newborn DGCs is either set to $N_{\text{newborn}}=119$ (corresponding to 17\% of newborn DGCs), or $N_{\text{newborn}}=35$ (corresponding to 5\% of newborn DGCs). The results with $\eta_{\text{mature}} = 0$ from the main text are repeated here for convenience.}
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    $N_{DGC}$ & $N_{\text{newborn}}$ & $\eta_{\text{mature}}$ & $P$ [\%] & $P_3$ [\%] & $P_4$ [\%] & $P_5$ [\%] \\[5pt]
\hline
& & & & & & \\
 100 & 21 & 0 & 94.56 & 90.50 & 98.17 & 95.18 \\[35pt]
 100 & 21 & 0.0025 & 93.83 & 90.79 & 99.08 & 91.48 \\[35pt]
 100 & 21 & 0.005 & 94.07 & 91.78 & 98.98 & 91.26 \\[35pt]
 100 & 21 & 0.0075 & 93.59 & 91.68 & 98.98 & 89.80 \\[35pt]
 100 & 21 & 0.01 & 92.34 & 88.61 & 98.88 & 89.35 \\[15pt]
 \hline
 & & & & & & \\
 700 & 119 & 0 & 95.49 & 90.59 & 98.17 & 98.09 \\[35pt]
 700 & 119 & 0.005 & 94.97 & 91.19 & 97.35 & 96.64 \\[35pt]
 700 & 35 & 0 & 90.88 & 88.51 & 97.05 & 86.77 \\[35pt]
 700 & 35 & 0.005 & 94.28 & 89.11 & 96.74 & 97.42 \\
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