Inspired by the principles of the biological nervous system, neuromorphic engineering has
brought a promising alternative approach to intelligence computing with high energy …

The von Neumann bottleneck and memory wall have posed fundamental limitations in
latency and energy consumption of modern computers based on von Neumann architecture …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Neuromorphic machines are intriguing for building energy-efficient intelligent systems,
where spiking neurons are pivotal components. Recently, memristive neurons with …

A self-organizing map (SOM) is a powerful unsupervised learning neural network for
analyzing high-dimensional data in various applications. However, hardware …

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while
silicon image sensors do not. The energy-budget mismatch between biological systems and …

Hopfield neural network, as a recurrent neural network, has been widely used to solve non-
deterministic polynomial time-hard problems. However, the network tends to get trapped into …

Spiking neuron circuits, responsible for encoding analog signals into spiking signals, are
crucial for conversion-based spiking neural networks (SNNs), enabling direct integration …

Neurons are basic elements of the human brain to transmit and process various kinds of
information. Besides the synaptic plasticity, the neuronal intrinsic plasticity (NIP) plays a vital …

A junctionless single-transistor neuron (JT-neuron) composed of vertically stacked multiple
nanowires (NWs) with a gate-all-around structure (GAA) is demonstrated to drive more …