Neuromorphic AI is developing spiking Neural Networks. Spiking neural networks can run different algorithms than neural networks. They have temporal properties and features. It requires less data and less energy than regular neural networks. Energy can drop 100 to 1000 times. Spiking neural networks thrives where it responds to real-world inputs in real time settings. …
1. A memristor has been proposed as an artificial synapse for emerging neuromorphic computing applications. (Nature Nanotechnology) To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform. An electrochemical metallization (ECM) memory typically based on silicon (Si), has demonstrated a good analogue …
Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artifical intelligence algorithms — based on the very slow brain dynamics — which outperform learning rates achieved to date by state-of-the-art learning algorithms. The researchers rebuild the bridge between …
Sandia Labs and other researchers have made recent breakthroughs in neuromorphic computing and the broader fields of organic electronics and solid-state electrochemistry.
Southampton researchers have improved the memristor. how they have pushed the memristor – a simpler and smaller alternative to the transistor, with the capability of altering its resistance and storing multiple memory states – to a new level of performance after experimenting with its component materials. They demonstrated a new memristor technology that can store …
Neuromorphic computing researchers have been working on the development of memory resistors, or memristors, which are resistors in a circuit that ‘remember’ their state even if you lose power. Today, most computers use random access memory (RAM), which moves very quickly as a user works but does not retain unsaved data if power is lost. …