The Design of Memristive Circuit for Affective Multi-Associative Learning

• Published in: IEEE transactions on biomedical circuits and systems 2020-04, Vol.14 (2), p.173-185
• Main Authors: Wang, Zilu, Wang, Xiaoping, Lu, Zezao, Wu, Weiguo, Zeng, Zhigang
• Format: Article
• Language: English
• Subjects: Affective associative learning; affective formation; Associative learning; Biomimetics; Circuit design; Electrical Equipment and Supplies; Emotions; Equipment Design; Humanoid; Humans; Inhibition (psychology); Integrated circuit modeling; Learning; Little Albert experiment; Machine Learning; Mathematical model; memristive circuit system; Memristors; Models, Neurological; Modules; Neurons; Neurons - physiology; Rats; Robotics; Robots; Synapses; Synapses - physiology; Synaptic strength; Threshold voltage; Vocabulary development
• ISSN: 1932-4545; 1940-9990
• DOI: 10.1109/TBCAS.2019.2961569

In this work, a memristive circuit with affective multi-associative learning function is proposed, which mimics the process of human affective formation. It mainly contains three modules: affective associative learning, affective formation, affective expression. The first module is composed of several affective single-associative learning circuits consisting of memristive neurons and synapses. Memristive neuron will be activated and output pulses if its input exceeds the threshold. After it is activated, memristive neuron can automatically return to the inactive state. Memristive synapse can realize learning and forgetting functions based on the signals from pre- and post-neurons. The learning rule is pre-neuron activated lags behind post-neuron for a short time; the forgetting rule is to repeatedly activate pre-neuron after the emotion is learned. The process of learning or forgetting corresponds to facilitating or inhibiting synaptic weight, that is, decreasing or increasing memristance continuously. Different voltage signals applied to memristors and different parameters of memristors would lead to different synaptic weights which indicate different affective association. The second module can convert affective signals to corresponding emotions. The formed emotions can be shown in a face by the third module. The simulation results in PSPICE show that the proposed circuit system can learn, forget and form emotions like human. If the proposed circuit is further used on a humanoid robot platform through further research, the robot will have the ability of affective interaction with human so that it can be effectively used in affective company and other aspects.