# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 3 Implementation  
### Improved BEL Neural Network  
In contrast to previous BEL-based models, we apply the fitness function in GA instead of reinforcement learning to update the weights of amygdala and orbitofrontal cortex in emotional learning. Therefore, we delete the reward signal in the BEL neural network. In addition, according to the biological interaction between amygdala and orbitofrontal cortex in the emotional learning, we add the bias for each part. The improved BEL-based neural network is shown in Figure 3, which consists of four common subsystems including thalamus, sensory cortex, orbitofrontal cortex and amygdala. Amygdala and orbitofrontal cortex are the two main subsystems, which are mainly responsible for emotional learning.  
The model is presented as a multiple input-single output architecture; amygdala receives \(m\) input vectors \(SI\) = [\(S_{1}\), \(S_{2}\),..., \(S_{m}\)] from the sensory cortex and \(A_{\text{th}}\) from the thalamus; \(A_{\text{th}}\) is calculated by Equation (4) [10]:  
\[A_{\text{th}}=\text{max}(S_{1},S_{2},...,S_{m}) \tag{4}\]  
Figure 3: Improved single-output brain-inspired emotional learning (BEL) network architecture.  
As shown in Figure 3, \(v_{i}\) is the amygdala weight, and \(b_{a}\) is the bias of amygdala neuron. \(E_{A}\) is the output of the amygdala, and it is calculated by Equation (5) [10]:  
\[\begin{array}{l}A_{i}=S_{i}\cdot v_{i},\quad i=1,2,...,m\\ A_{m+1}=A_{th}\cdot v_{m+1}\\ E_{A}=\sum\limits_{i=1}^{m+1}A_{i}+b_{a}=\sum\limits_{i=1}^{m}S_{i}\cdot v_{i} +A_{th}v_{m+1}+b_{a}\end{array} \tag{5}\]  
Furthermore, the orbitofrontal cortex receives the input patterns from the sensory cortex. \(E_{O}\) is the output of the orbitofrontal cortex that is used to inhibit the amygdala's output, which is calculated by Equation (6) [10]:  
\[\begin{array}{l}O_{i}=S_{i}\cdot w_{i},\quad i=1,2,...,m\\ E_{O}=\sum\limits_{i=1}^{m}O_{i}+b_{o}\ =\sum\limits_{i=1}^{m}S_{i}\cdot w_{i}+b_{o} \end{array} \tag{6}\]  
where \(w_{i}\) are orbitofrontal cortex weights and \(b_{o}\) is the bias of orbitofrontal cortex neuron.  
Finally, the final output is simply calculated by Equation (7) [10]:  
\[E=E_{A}-E_{O} \tag{7}\]  
where \(E\) is the final output that represents the correct amygdala response.  
The improved single-output BEL neural network can be learned by pattern-target examples; it is model free and can be utilized in classification application. The number of features in the input vector determines the number of neurons in the thalamus and sensory cortex units, and the number of classes determines the number of orbitofrontal cortex and amygdala units. As a result, the improved BEL model can be extended to generalize in multi-classification applications, and the architecture is shown in Figure 4.  
Figure 4: Improved multiple-output BEL network architecture.  
In the proposed \(m\)-\(n\) BEL network as shown in Figure 4, \(m\) is the number of inputs, and \(n\) is the number of outputs; there are \(n\) amygdala-orbitofrontal cortex parts; in this network, each output unit is associated with one amygdala-orbitofrontal cortex part that interacts separately. Although the distinctive feature of BEL network is fast learning, the network easily is trapped into local minimum. Therefore, the performance of classification accuracy needs to be improved.