This paper presents an experimental demonstration of associative memory using memristive neural networks. The authors show that a memristor, a device with memory properties, can function as an artificial synapse, capable of storing a continuous set of states, being "plastic" based on neuronal activity, and remembering its past dynamical history. They built a memristor emulator using simple and inexpensive components, which realizes all required synaptic properties. The experiment demonstrates the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens new possibilities for understanding neural processes using memory devices and is an important step toward reproducing complex learning, adaptive, and spontaneous behavior with electronic neural networks. The paper discusses the challenges of implementing electronic synapses, which need to be flexible, plastic, and capable of storing continuous values. Previous approaches have used various circuit elements, but they are limited in size and functionality. The authors propose a flexible platform for simulating different types of memristors and experimentally show that a memristor can indeed function as a synapse. They developed electronic versions of neurons and synapses that can be easily tuned to biological functions and are fabricated using inexpensive components, making them suitable for any electronic laboratory. The authors built an electronic neural network for associative memory, demonstrating that the circuit can learn to associate different input signals. The network consists of three neurons connected by two memristive synapses. The experiment shows that the circuit can learn to associate the "sight of food" with the "sound" through repeated training, demonstrating the Hebbian rule that "neurons that fire together, wire together." The results show that the electronic neurons and synapses can represent important functionalities of their biological counterparts and, when combined in networks, can give rise to associative memory, a key function of the brain. The study highlights the potential of memristive devices in creating complex neural networks that can adapt to incoming signals and make decisions based on correlations between different memories.This paper presents an experimental demonstration of associative memory using memristive neural networks. The authors show that a memristor, a device with memory properties, can function as an artificial synapse, capable of storing a continuous set of states, being "plastic" based on neuronal activity, and remembering its past dynamical history. They built a memristor emulator using simple and inexpensive components, which realizes all required synaptic properties. The experiment demonstrates the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens new possibilities for understanding neural processes using memory devices and is an important step toward reproducing complex learning, adaptive, and spontaneous behavior with electronic neural networks. The paper discusses the challenges of implementing electronic synapses, which need to be flexible, plastic, and capable of storing continuous values. Previous approaches have used various circuit elements, but they are limited in size and functionality. The authors propose a flexible platform for simulating different types of memristors and experimentally show that a memristor can indeed function as a synapse. They developed electronic versions of neurons and synapses that can be easily tuned to biological functions and are fabricated using inexpensive components, making them suitable for any electronic laboratory. The authors built an electronic neural network for associative memory, demonstrating that the circuit can learn to associate different input signals. The network consists of three neurons connected by two memristive synapses. The experiment shows that the circuit can learn to associate the "sight of food" with the "sound" through repeated training, demonstrating the Hebbian rule that "neurons that fire together, wire together." The results show that the electronic neurons and synapses can represent important functionalities of their biological counterparts and, when combined in networks, can give rise to associative memory, a key function of the brain. The study highlights the potential of memristive devices in creating complex neural networks that can adapt to incoming signals and make decisions based on correlations between different memories.