#artificial synapse

[neurosciencenews]

If Only AI Had a Brain

Researchers have developed an "artificial synapse" that does not process information like a digital computer but rather mimics the analog way the human brain completes tasks.

The research is in Advanced Materials. (full access paywall)

[ifreedawn]

New Post has been published on Scientia

Engineers design artificial-intelligence synapse for “brain-on-a-chip” hardware

Engineers design artificial synapse for “brain-on-a-chip” hardware Design is major stepping stone toward portable artificial-intelligence devices. Scientia — When it comes to processing power, the human brain just can’t be beat. Packed within the squishy, football-sized organ are somew...

https://www.freedawn.co.uk/scientia/2018/01/29/engineers-design-ai-synapse-brain-chip-hardware/

[outoftheboxscience-blog]

Memristor: Artificial Synapse That Can Learn Autonomously

Researchers from the National Centre for Scientific Research (CNRS) in Thales, the University of Bordeaux in Paris-Sud, and Evry have created an artificial synapse called a memristor directly on a chip. This leads the way for intelligent systems that will use less energy and take less time to learn autonomously.

The Memristor Works Similar To Synapses In Human Brain

The human’s brain has trillions of synapses, which serve as connections between neurons. Person’s ability to learn directly depends on the ability of synapses to reconfigure the strength by which they connect neurons. The overall distribution of the synaptic strengths provides the neural network with memory. The more the synapse is stimulated, the more the neuronal connection is reinforced, resulting in improved learning.

This mechanism was an inspiration for researchers to develop an artificial synapse called memristor. It consists of a thin ferroelectric layer set between two electrodes. Layer’s resistance can be tuned using voltage pulses similar to those in neurons. The memristor’s capacity for learning is based on this adjustable resistance. Therefore, if the resistance is low the synaptic connection will be strong. Oppositely, if the resistance is high the connection will be weak.

Artist’s impression of the electronic synapse: the particles represent electrons circulating through the oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses. Image credit: © Sören Boyn / CNRS/Thales physics joint research unit

The idea of the memristor is not new. It was first conceptualized in the 1970s by circuit theorist Leon Chua and subsequently built in 2008. Originally, the memristor was a hypothetical non-linear passive two-terminal electrical component relating electric charge and magnetic flux linkage. In 2008, researchers at HP Labs have solved a decades-old mystery by proving the existence of a fourth basic element in integrated circuits that could make it possible to develop computers that turn on and off like an electric light. Finally, the development of artificial synapse takes this matter to the next level.

https://www.youtube.com/watch?v=3OAJr-2ljNc

Now, when researchers manage to create 45 memristors which were able to learn to detect simple patterns without any assistance, the next goal is to develop neural networks on a chip that contain hundreds of these ferroelectric memristors.

Doubtless, neural networks are responsible for some of the biggest breakthroughs in AI. Probably one day we will see AI systems that are able to learn just as well as the human brain.

#ArtificialSynapse, #FerroelectricLayer, #HPLab, #Memristor, #NeuralNetwork https://outoftheboxscience.com/tech/artifical-intelligence/memristor-artificial-synapse-can-learn-autonomously/

[thoths-foundry-blog]

A new organic artificial synapse made by Stanford researchers could support computers that better recreate the way the human brain processes information. It could also lead to improvements in brain-machine technologies.

For all the improvements in computer technology over the years, we still struggle to recreate the low-energy, elegant processing of the human brain. Now, researchers at Stanford University and Sandia National Laboratories have made an advance that could help computers mimic one piece of the brain’s efficient design – an artificial version of the space over which neurons communicate, called a synapse.

“It works like a real synapse but it’s an organic electronic device that can be engineered,” said Alberto Salleo, associate professor of materials science and engineering at Stanford and senior author of the paper. “It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics.”

The new artificial synapse, reported in the Feb. 20 issue of Nature Materials, mimics the way synapses in the brain learn through the signals that cross them. This is a significant energy savings over traditional computing, which involves separately processing information and then storing it into memory. Here, the processing creates the memory.

This synapse may one day be part of a more brain-like computer, which could be especially beneficial for computing that works with visual and auditory signals. Examples of this are seen in voice-controlled interfaces and driverless cars. Past efforts in this field have produced high-performance neural networks supported by artificially intelligent algorithms but these are still distant imitators of the brain that depend on energy-consuming traditional computer hardware.

[resetnetwork-blog]

Well, this happened. Engineering researchers the University of Southern California have made an insanely major breakthrough in the use of nanotechnologies for the construction of a synthetic brain; they've built a carbon nanotube synapse. The circuit's behavior (in tests) reproduces the function of a neuron, the building block of the brain. Although replacing biological neurons in our brains (which contain, for context, 100 billion neurons with 10,000 synapses each) with such a circuit is still decades away, this research has major, long-term implications. 

An interesting aside: the USC researchers claim the catalyst for such a monumental finding lay in the interdisciplinary nature of their team. With nanotechnology researchers collaborating with circuit-designing engineers, the complex problem of capturing brain function gave more readily.