Recently, traditional computing systems based on the von Neumann architecture are facing
the very well-known problems of memory-access bottleneck and energy efficiency wall. The …

In this work, I present a CMOS implementation of a neuromorphic system that aims to mimic
the behavior of biological neurons and synapses in the human brain. The synapse is …

Conventional neuro-computing architectures and artificial neural networks have often been
developed with no or loose connections to neuroscience. As a consequence, they have …

Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …

The field of neuromorphic computing is in a period of active exploration. While many tools
have been developed to simulate neuronal dynamics or convert deep networks to spiking …

Second order memristors have shown to be able to mimic some specific features of neuron
synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be …

The hardware implementation of neuromorphic system requires energy and area-efficient
hardware. Memristor-based hardware architectures is a promising approach that naturally …

By following the big data revolution, neuromorphic computing makes a comeback for its
great potential in information processing capability. Despite of many types of architectures …

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to
overcome the intrinsic energy and speed issues of traditional von Neumann based …

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for
decades, taking advantage of its massive parallelism and sparse information coding …