A Multi-functional Memristive Pavlov Associative Memory Circuit Based on Neural Mechanisms

Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks

Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.

The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks

In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.

Memristive neural network circuit implementation of associative learning with overshadowing and blocking

In the field of second language acquisition, overshadowing and blocking by cue competition effects in classical conditioning affect the learning and expression of human cognitive associations. In this work, a memristive neural network circuit based on neurobiological mechanisms is proposed, which consists of synapse module, neuron module, and control module. In particular, the designed network introduces an inhibitory interneuron to divide memristive synapses into excitatory and inhibitory memristive synapses, so as to mimic synaptic plasticity better. In addition, the proposed circuit can implement six functions of second language acquisition conditioning, including learning, overshadowing, blocking, recovery from overshadowing, recovery from blocking, and long-term effect of overshadowing over time leading to blocking. Overshadowing, which denotes that the more salient stimulus overshadows the learning of the less salient stimulus when two stimuli differ in salience, reduces the associative strength acquired by the less salient stimulus. Blocking, which indicates that pretraining on one stimulus blocks learning about a second stimulus, inhibits the associative strength acquired by a second stimulus. Finally, the correctness and effectiveness of implementing functions mentioned above are verified by the simulation results in PSPICE. Through further research, the proposed circuit is applied to bionic devices such as social robots or educational robots, which can address language and cognitive disorders via assisted learning and training.

Memristor-Based Neural Network Circuit With Multimode Generalization and Differentiation on Pavlov Associative Memory

Most of the classical conditioning laws implemented by existing circuits are involved in learning and forgetting between only three neurons, and the problems between multiple neurons are not considered. In this article, a multimode generalization and differentiation circuit for the Pavlov associative memory is proposed based on memristors. The designed circuit is mainly composed of voltage control modules, synaptic neuron modules, and inhibition modules. The secondary differentiation is accomplished through the process of associative learning and forgetting among multiple neurons. The process of multiple generalization and differentiation is realized based on the nonvolatility and thresholding properties of memristors. The extinction inhibition and differentiation inhibition in forgetting is considered through the inhibition modules. The Pavlov associative memory neural network with multimodal generalization and differentiation may provide a reference for the further development of brain-like intelligence.

Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory

Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.

Efficient Memristive Circuit Design of Neural Network-Based Associative Memory for Pavlovian Conditional Reflex

The brain's learning and adaptation processes heavily rely on the concept of associative memory. One of the most basic associative learning processes is classical conditioning. This work presents a memristive neural network-based associative memory system. The system can emulate Pavlovian conditioning principles including acquisition, extension, generalization, differentiation, and spontaneous recovery that have not been considered in most of the previous counterparts. The proposed circuit can emulate these principles thanks to the resistance-changing characteristics of the memristor. Generalization has been achieved by providing both unconditional and neutral stimuli to the network to reduce the memristance of the memristor. Differentiation has been attained by employing unconditional and conditional stimuli in a training scheme to obtain a certain memristance that causes the network to respond differently to both stimuli. A revival of an exterminated stimuli is also done by increasing the synaptic weight of the system. Compared to previous designs, the proposed memristive circuit can implement all the functions of conditional reflex. Our rigorous simulations demonstrated that the proposed memristive system can condition neutral stimuli, show generalization between similar stimuli, distinguish dissimilarities between the generalized stimuli, and recover faded stimuli.

Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning

Classical conditioning (CC) and operant conditioning (OC), also known as associative memory, are two of the most fundamental and critical learning mechanisms in the biological brain. However, the existing designs of associative memory memristive circuits mainly focus on CC, and few studies have used memristors to imitate OC at the behavioral level, as well as the OC-CC cascaded associative memories that are widespread in biological learning processes. This work proposes an OC-CC cascaded circuit composed of OC and CC circuits. With the OC memristive circuit, bio-like functions such as random exploration, feedback learning, experience memory, and experience-based decision-making are achieved, which enables the circuit to continuously reshape its own memories and actions to adapt to changing environments. By cascading it with the CC memristive circuit that has the functions of associative learning, forgetting, generalization, and differentiation, the OC-CC cascaded circuit implements richer associative memories and has stronger environmental adaptability. Finally, the proposed circuits can perform on-line in-situ learning and in-memory computing. This is a more brain-like processing method, which is different from the von Neumann architecture. The simulation results of the proposed circuits in PSPICE show that they can simulate the above functions and have advantages in power consumption and hardware overhead. This work provides a possible realization idea for large-scale bionic learning.