Related papers: Brain-inspired computing with fluidic iontronic na…
The first report on ion transport through atomic sieves of atomically-thin 2D material is provided to solve critical limitations of electrochemical random-access memory (ECRAM) devices.
The basic units in our brain are neurons and each neuron has more than 1000 synapse connections. Synapse is the basic structure for information transfer in an ever-changing manner, and short-term plasticity allows synapses to perform…
Replacing electrons with photons is a compelling route towards light-speed, highly parallel, and low-power artificial intelligence computing. Recently, all-optical diffractive neural deep neural networks have been demonstrated. However, the…
Fluid and ionic transport at nanoscale recently highlighted a wealth of exotic behaviours. However, the artificial nanofluidic devices are still far from the advanced functionalities existing in biological systems, such as electrically and…
Photonic neuromorphic computing may offer promising applications for a broad range of photonic sensors, including optical fiber sensors, to enhance their functionality while avoiding loss of information, energy consumption, and latency due…
We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the…
Ion channels orchestrate the communication between cells and their environment. These are special proteins capable of changing their shape to allow ions to pass through membranes in response to stimuli like membrane tension. As the letters…
Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neumann architectured machines. In particular, spiking neural…
Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to…
Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in…
Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tuneable conductivity properties enable the imitation of synaptic behaviour. Multipore nanofluidic memristors have shown their…
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially…
A chemical discrimination system based on photonic reservoir computing is demonstrated experimentally for the first time. The system is inspired by the way humans perceive and process visual sensory information. The electro-optical…
In charged nanopores, ionic diffusion current reflects the ionic selectivity and ionic permeability of nanopores which determines the performance of osmotic energy conversion, i.e. the output power and efficiency. Here, theoretical…
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks.…
Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent…
The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence…
Physical Reservoir Computing (PRC) offers an efficient paradigm for processing temporal data, yet most physical implementations are static, limiting their performance to a narrow range of tasks. In this work, we demonstrate in silico that a…
The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more…
Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and…