A Memcapacitor Biomimetic Circuit Realizing Classical Conditioning and Fear Learning

Abstract

Most associative memory neural networks are realized by memristor, but memcapacitor which can simulate the biological behavior of neuron preferably has better characteristics than memristor to realize the pavlov associative memory neural networks. This article introduces a novel neural network paradigm with memcapacitors, encompassing thirteen classical conditional reflection functions. These include pivotal aspects such as learning, forgetting, time interval conditioning, latent inhibition, time delay conditioning, facilitation, blocking, secondary conditioning, and fear learning, meticulously validated through simulation results. The proposed architecture interconnects nine analogous neuron modules through diverse synapses, culminating in a meticulously designed circuit. This memcapacitor biomimetic circuit not only achieves the implementation of thirteen classical conditional reflections but also boasts scalability, offering versatility in its application. Particularly noteworthy is its potential application in marine debris collection robots, showcasing adaptability in working intricate oceanic traffic conditions.