Each neuron of the GWR has a weight vector ${w}_j$ that represents a prototype of the perceived information. In our scenario, we feed the GWR with perceived PA values. A newly perceived PA value will be associated with a best-matching unit (BMU) $b$, which is calculated by minimizing the distances between the perceived PA value and all the neurons on the GWR. Given a set of $N$ neurons, $b$ concerning the input ${x}\in\mathbb{R}^n$ is computed as:

$$% \label{eq:GetB}
\begin{aligned}
b = \arg\min_{j\in N} \left(\Vert {x} - {w}_j  \Vert^2 \right).
\end{aligned}$$

When the BMU is found, new connections are created between the BMU and the second-BMU. Every neuron that is connected to the BMU is its topological neighbour. Each neuron has an aging mechanism, represented by an habituation counter $h_i \in [0,1]$, which represents how close this neuron was to the BMU, and thus, how important it is for representing the current input.

The habituation rule is given by:

$$\begin{aligned}
\Delta h_i=\tau_i \cdot \kappa \cdot (1-h_i)-\tau_i.
\end{aligned}$$

where $\kappa$ and $\tau_i$ are constants that control the decreasing behavior of the habituation counter [@Marsland2002]. To establish whether a neuron is habituated, its habituation counter $h_i$ must be smaller than a given habituation 