NDLI: Configuring the stochastic Helmholtz machine for subcortical emotional learning

Content Provider	IEEE Xplore Digital Library
Author	Chi-Yung Yau Burn, K. Wermter, S.
Copyright Year	2010
Description	Author affiliation: Institute for Knowledge Technology, Department of Informatics, University of Hamburg, Germany (Wermter, S.) \|\| Faculty of Applied Sciences, Department of Computing, Engineering and Technology, University of Sunderland, U.K. (Chi-Yung Yau; Burn, K.)
Abstract	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 [1] 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.
Starting Page	1
Ending Page	8
File Size	1168067
Page Count	8
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596285
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Feature extraction Neurons Mathematical model Visualization Training Data models
Content Type	Text
Resource Type	Article

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