(?whitehoune/Shutterstock) Heads up, humans.

Artificial intelligence may get smarter.

An international team of scientists has developed a new artificial intelligence system to synthesize synapses using neural network models.

In artificial neural networks, computing systems are designed to simulate the functions of the human brain, with digital neurons and synapses replicating the functions of their biological counterparts.

In this context, synapses act as gateways for neurons, whether synthetic or biological, to pass information and signals to each other.

They are the connective tissue in biological and artificial neural networks.

It is estimated that the typical human nervous system contains more than 100 trillion synapses.

While scientists have achieved remarkable success with artificial neural networks, contemporary artificial intelligence systems suffer from specific limitations.

In the mammalian brain, synapses can accommodate both inhibitory and excitatory signals.

But artificial synapses made of nanoelectronics can only process one signal at a time.

As a result, AI systems can only operate at half speed.

Until now.

Researchers in the United States and China have developed a synthetic synapse capable of processing both signals, reconfiguring itself on the fly, according to new research published this week in the journal ACS Nano.

Funding for the project was provided by the National Science Foundation and the Army Research Office.

"These new artificial synapses allow the same synapse to be reconfigured into excitatory or inhibitory modes, which was not possible in previous solid-state artificial synapse devices," said co-author Wang Han of the University of Southern California.

"This new functional flexibility will be important for enabling more complex artificial neural networks that can be dynamically reconfigured like our brains." KdSPE "KDSPs" "KDSPE" "KDSPs" Related: Meet the Web's Most

HAN, the engineer behind the viral artificial neural network "KDSPE", said that in the human brain, excitatory responses generally make the brain more excited and alert, while inhibitory responses make it more calm and relaxed in the nervous system.

At a distance, excitatory responses cause muscles to contract, and inhibitory responses cause muscles to relax. The new artificial synapses allow similar functionality in computer systems, as artificial neural networks use biological synapses to process chemical and electrical signals.

Synthetic synapses to process digital information "In artificial neural networks, excitatory signals strengthen certain connections in the network, and inhibitory responses weaken such connections," Wang said. This biological simulation has implications for artificial neural networks.

The development of a generation of cognitive capabilities is critical "to simulate more complex neural systems, making the system potentially more intelligent and versatile," Wang said. You'll need a PhD or three to wrap your head around it.

But Wang suggested a car metaphor: "It's like one is the accelerator and the other is the brake, and the two work together to ensure proper function and stability of brain activity - a car," Wang said, which is closer to that of a biological brain.

Features. Originally published on Seeker.