Configuring the stochastic Helmholtz machine for subcortical emotional learning

Emotional learning involves two stages. The first is to acquire reinforcers from stimuli and the second is to associate such reinforcers with emotional responses. Both stages can be found occurring in the amygdala. LeDoux´s fear circuit model suggests two routes, a subcortical route and a cortical route, for emotional information entering the amygdala for associative learning. It can be used to explain how the actual recognition of emotions from facial expressions can be processed in the brain. Based on the model, a neural architecture is proposed using the stochastic Helmholtz machine (SHM) with the wake-sleep algorithm. In this paper, the results of three experiments about the subcortical emotional learning are reported, where different configurations of SHMs are involved. The first two experiments are to identify a suitable way to allow behavioural responses entering the central nucleus of the amygdala for association. However, both experiments show symptoms of overfitting, where some weights and biases of neurons are observed that will unusually increase during training. Therefore, the final experiment is designed to maintain the range of weights between -1 and +1 in order to solve the overfitting problem. The last experiment shows that the neural architecture with the new weight policy holds a lot of potential for modelling subcortical learning.