Brain-inspired neuromorphic computing is attracting strong interest thanks to its advantages of massive parallelism, high energy efficiency, and cognitive functions. As the cellular unit for learning and memory, the emulation of synapse and neuron is the key step toward neuromorphic computing. Memristive systems have been widely recognized as the promising hardware implementations of neuromorphic computing because of their functional resemblance to the biological counterparts (e.g., synapses and neurons).
Over the past decades, many great efforts have been devoted to the development of memristive materials, devices, and circuits for artificial neural networks. However, there are still extensive challenges before demonstrating the practical hardware of memristive neuromorphic computing. The challenges may include the switching reliability of memristor devices, the biorealistic realization of synaptic functions, the framework and algorithm of the spiking neuron networks, and so on. To overcome those obstacles, the study of memristive physics, materials, and circuits are still necessary. This Research Topic will cover topics that involve the two-/three-terminal memristive systems based on inorganic/organic materials in the context of neuromorphic computing.
In this Research Topic, we welcome articles focused on the developments of memristive neuromorphic computing.
Some potential themes of interest for this Research Topic include but are not limited to:
• Memristors and resistive switching materials and devices
• Modeling of memristive materials and devices
• Artificial synapse/neuron based on memristive system
• Artificial neural networks based on memristive device and circuit
• Memcomputing: fusion of memory and computing
Brain-inspired neuromorphic computing is attracting strong interest thanks to its advantages of massive parallelism, high energy efficiency, and cognitive functions. As the cellular unit for learning and memory, the emulation of synapse and neuron is the key step toward neuromorphic computing. Memristive systems have been widely recognized as the promising hardware implementations of neuromorphic computing because of their functional resemblance to the biological counterparts (e.g., synapses and neurons).
Over the past decades, many great efforts have been devoted to the development of memristive materials, devices, and circuits for artificial neural networks. However, there are still extensive challenges before demonstrating the practical hardware of memristive neuromorphic computing. The challenges may include the switching reliability of memristor devices, the biorealistic realization of synaptic functions, the framework and algorithm of the spiking neuron networks, and so on. To overcome those obstacles, the study of memristive physics, materials, and circuits are still necessary. This Research Topic will cover topics that involve the two-/three-terminal memristive systems based on inorganic/organic materials in the context of neuromorphic computing.
In this Research Topic, we welcome articles focused on the developments of memristive neuromorphic computing.
Some potential themes of interest for this Research Topic include but are not limited to:
• Memristors and resistive switching materials and devices
• Modeling of memristive materials and devices
• Artificial synapse/neuron based on memristive system
• Artificial neural networks based on memristive device and circuit
• Memcomputing: fusion of memory and computing
