Resistive random access memory (RRAM) devices are receiving increasing extensive
attention due to their enhanced properties such as fast operation speed, simple device …

In today's era of big‐data, a new computing paradigm beyond today's von‐Neumann
architecture is needed to process these large‐scale datasets efficiently. Inspired by the …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Neuromorphic computing systems, which use electronic synapses and neurons, could
overcome the energy and throughput limitations of today's computing architectures …

Memristors can be employed to mimic biological neural synapses or to describe
electromagnetic induction effects. To exhibit the threshold effect of electromagnetic …

Biological synapses store and process information simultaneously by tuning the connection
between two neighboring neurons. Such functionality inspires the task of hardware …

In an increasingly data-rich world the need for developing computing systems that cannot
only process, but ideally also interpret big data is becoming continuously more pressing …

In this review, a comprehensive survey of different oxide-based resistive random-access
memories (RRAMs) for neuromorphic computing is provided. We begin with the history of …

Neuromorphic architectures are in the spotlight as promising candidates for substituting
current computing systems owing to their high operation speed, scale‐down ability, and …

The synaptic weight modification depends not only on interval of the pre‐/postspike pairs
according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing …