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

Simulating biological synaptic functionalities through artificial synaptic devices opens up an
innovative way to overcome the von Neumann bottleneck at the device level. Artificial …

Neuromorphic computing can potentially solve the von Neumann bottleneck of current
mainstream computing because it excels at self‐adaptive learning and highly parallel …

Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing
systems based on analog resistive switching memory (RSM) devices have drawn great …

The growing demands of brain science and artificial intelligence create an urgent need for
the development of artificial neural networks (ANNs) that can mimic the structural, functional …

Memristor, based on the principle of biological synapse, is recognized as one of the key
devices in confronting the bottleneck of classical von Neumann computers. However …

What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

We introduce Equilibrium Propagation, a learning framework for energy-based models. It
involves only one kind of neural computation, performed in both the first phase (when the …

Get in-depth understanding of each part of visual pathway yields insights to conquer the
challenges that classic computer vision is facing. Here, we first report the bioinspired striate …