The paper "Experimental demonstration of associative memory with memristive neural networks" by Yuriy V. Pershin and Massimiliano Di Ventra explores the use of memristors to simulate the essential properties of synapses in both real and artificial neural systems. The authors demonstrate that a memristor can serve as an artificial synapse, capable of remembering its past dynamical history, storing a continuous set of states, and adapting to pre-synaptic and post-synaptic neuronal activity. They built a memristor emulator using simple and inexpensive off-the-shelf components, which can realize all required synaptic properties. The key experiment involves a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This network successfully demonstrates the formation of associative memory, where the output neuron fires in response to both input neurons when they are simultaneously activated, achieving the Hebbian learning rule. The study opens new possibilities for understanding neural processes using memory devices and advancing the reproduction of complex learning, adaptive, and spontaneous behaviors in electronic neural networks.The paper "Experimental demonstration of associative memory with memristive neural networks" by Yuriy V. Pershin and Massimiliano Di Ventra explores the use of memristors to simulate the essential properties of synapses in both real and artificial neural systems. The authors demonstrate that a memristor can serve as an artificial synapse, capable of remembering its past dynamical history, storing a continuous set of states, and adapting to pre-synaptic and post-synaptic neuronal activity. They built a memristor emulator using simple and inexpensive off-the-shelf components, which can realize all required synaptic properties. The key experiment involves a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This network successfully demonstrates the formation of associative memory, where the output neuron fires in response to both input neurons when they are simultaneously activated, achieving the Hebbian learning rule. The study opens new possibilities for understanding neural processes using memory devices and advancing the reproduction of complex learning, adaptive, and spontaneous behaviors in electronic neural networks.