The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

The study of dynamics on artificial neurons and neuronal networks is of great significance to
understand brain functions and develop neuromorphic systems. Recently, memristive …

The close replication of synaptic functions is an important objective for achieving a highly
realistic memristor-based cognitive computation. The emulation of neurobiological learning …

Spiking neural networks (SNNs) are biologically plausible and computationally powerful.
The current computing systems based on the von Neumann architecture are almost the …

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 …

Although data processing technology continues to advance at an astonishing rate,
computers with brain-like processing capabilities still elude us. It is envisioned that such …

To successively implement synaptic memristor device in the neuromorphic computing
system, it is essential to perform a variety of synaptic characteristics with low power …

The training of neural networks involves numerous operations on the weight matrix. If neural
networks are implemented in hardware, all weights will be updated in parallel. However …

Spiking neural networks (SNNs) are gaining increasing attention for their biological
plausibility and potential for improved computational efficiency. To match the high spatial …

As memristor-simulating synaptic devices have become available in recent years, the
optimization on non-linearity degree (NL, related to adjacent conductance values) is …