threshold $t_h$.

The network is initialized with two neurons and, at each learning iteration, it inserts a new neuron whenever the activity of the network when a confidence $c$ is calculated, of a habituated neuron, is smaller than a given threshold $t_a$, i.e., a new neuron is created if $a(c)<t_a$ and $h_c<t_h$. The activity of the network is given by:

$$\begin{aligned}
a(c) = \exp(-\left(\Vert {x} - {w}_j  \Vert^2 \right)
\end{aligned}$$

For each perceived PA value, the network updates, in an online manner, the state of the neurons by updating the BMUs and their neighbors, or adding new neurons. Neurons that are old, with a smaller habituation counter than a certain threshold, are removed from the network. That guarantees a dynamic behavior that represents the PA values as soon as they are perceived, and although the model has not a direct temporal processing mechanism, such as recurrent connections, we are able to maintain a temporal relation on the perceived PA values by adding neurons that represent PA values never perceived before and removing neurons which represent PA values that were not perceived anymore.

![Examples of the Q-values (a), confidence (b), and mood (c) readings of the same agent on the same 