With imprecise chips to the artificial brain

Which circuits and chips are suitable for building artificial brains using the least possible amount of power? This is the question that Junior Professor Dr. Elisabetta Chicca from the Center of Excellence Cognitive Interaction Technology (CITEC) has been investigating in collaboration with colleagues from Italy and Switzerland. A surprising finding: Constructions that use not only digital but also analog compact and imprecise circuits are more suitable for building artificial nervous systems, rather than arrangements with only digital or precise but power-demanding analog electronic circuits. The study will be published in the scientific journal ‘Proceedings of the IEEE’. A preview was published online on Thursday, 1 March 2014.

Elisabetta Chicca is the head of the research group ‘Neuromorphic Behaving Systems’. One of the aims of her work is to make robots and other technical systems as autonomous and capable of learning as possible. The artificial brains that she and her team are developing are modelled on the biological nervous systems of humans and animals. ‘Environmental stimuli are processed in the biological nervous systems of humans and animals in a totally different way to modern computers’, says Chicca. ‘Biological nervous systems organise themselves; they adapt and learn. In doing so, they require a relatively small amount of energy in comparison with computers and allow for complex skills such as decision-making, the recognition of associations and of patterns.’

The neuroinformatics researcher is trying to utilise biological principles to build artificial nervous systems. Dr. Chicca and her colleagues have been investigating which type of circuits can simulate synapses electronically. Synapses serve as the ‘bridges’ that transmit signals between nerve cells. Stimuli are communicated through them and they can also save information. Furthermore, the research team have analysed which type of circuit can imitate the so-called plasticity of the biological nerves. Plasticity describes the ability of nerve cells, synapses and cerebral areas to adapt their characteristics according to use. In the brains of athletes, for example, certain cerebral areas are more strongly connected than in non-athletes.

The four researchers also offer solutions for the control of artificial nervous systems. They present software on the basis of which programmes can be written that can control the circuits and chips of an ‘electronic brain’.

Blueprint for an artificial brain

Scientists have long been dreaming about building a computer that would work like a brain. This is because a brain is far more energy-saving than a computer, it can learn by itself, and it doesn’t need any programming. Privatdozent [senior lecturer] Dr. Andy Thomas from Bielefeld University’s Faculty of Physics is experimenting with memristors – electronic microcomponents that imitate natural nerves. Thomas and his colleagues proved that they could do this a year ago. They constructed a memristor that is capable of learning. Andy Thomas is now using his memristors as key components in a blueprint for an artificial brain. He will be presenting his results at the beginning of March in the print edition of the prestigious Journal of Physics published by the Institute of Physics in London.

Memristors are made of fine nanolayers and can be used to connect electric circuits. For several years now, the memristor has been considered to be the electronic equivalent of the synapse. Synapses are, so to speak, the bridges across which nerve cells (neurons) contact each other. Their connections increase in strength the more often they are used. Usually, one nerve cell is connected to other nerve cells across thousands of synapses.

Like synapses, memristors learn from earlier impulses. In their case, these are electrical impulses that (as yet) do not come from nerve cells but from the electric circuits to which they are connected. The amount of current a memristor allows to pass depends on how strong the current was that flowed through it in the past and how long it was exposed to it.

Andy Thomas explains that because of their similarity to synapses, memristors are particularly suitable for building an artificial brain – a new generation of computers. ‘They allow us to construct extremely energy-efficient and robust processors that are able to learn by themselves.’ Based on his own experiments and research findings from biology and physics, his article is the first to summarize which principles taken from nature need to be transferred to technological systems if such a neuromorphic (nerve like) computer is to function. Such principles are that memristors, just like synapses, have to ‘note’ earlier impulses, and that neurons react to an impulse only when it passes a certain threshold.

Thanks to these properties, synapses can be used to reconstruct the brain process responsible for learning, says Andy Thomas.

With Evolved Brains, Robots Creep Closer To Animal-Like Learning

The most nightmare-inducing characteristic of Big Dog, DARPA’s robotic military mule, might be the way it moves so stiffly, yet unrelentingly, over treacherous battleground. Turns out the repetitive mechanical gait that calls to mind some coming robopocalypse is also a huge headache for Big Dog’s makers—and lots of the big thinkers behind walking bots envisioned for everyday domestic use.

Units like Big Dog move so awkwardly because of their rudimentary brains, which require pre-programming for every little action. A four-legged walking bot could jump smoothly over rocks or weave through trees with the fluid grace and reflexes of a cheetah—if it only had a better brain. One that was more animal-like. Thanks to breakthroughs in understanding how biological brains evolve, a team of robotic researchers say they’re close.

“We are working on evolving brains that can be downloaded onto a robot, wake up, and begin exploring their environment to figure out how to accomplish the high-level objectives we give them (e.g. avoid getting damaged, find recharging stations, locate survivors, pick up trash, etc.),” says Jeffrey Clune, Assistant Professor of Computer Science at the University of Wyoming, who is part of the robotics team.

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A More Human Artificial Brain

Staying on task

Its full name is the Semantic Pointer Architecture Unified Network, but Spaun sounds way more epic. It’s the latest version of a techno brain, the creation of a Canadian research team at the University of Waterloo.

So what makes Spaun different from a mindboggingly smart artificial brain like IBM’s Watson? Put simply, Watson is designed to work like a supremely powerful search engine, digging through an enormous amount of data at breakneck speed and using complex algorithms to derive an answer. It doesn’t really care about how the process works; it’s mainly about mastering information retrieval.

But Spaun tries to actually mimic the human brain’s behavior and does so by performing a series of tasks, all different from each other. It’s a computer model that can not only recognize numbers with its virtual eye and remember them, but also can manipulate a robotic arm to write them down.

Spaun’s “brain” is divided into two parts, loosely based on our cerebral cortex and basil ganglia and its simulated 2.5 million neurons–our brains have 100 billion–are designed to mimic how researchers think those two parts of the brain interact.

Say, for instance, that its “eye” sees a series of numbers. The artificial neurons take that visual data and route it into the cortex where Spaun uses it to perform a number of different tasks, such as counting, copying the figures, or solving number puzzles.

Soon it will be forgetting birthdays

But there’s been an interesting twist to Spaun’s behavior. As Francie Diep wrote in Tech News Daily, it became more human than its creators expected.

Ask it a question and it doesn’t answer immediately. No, it pauses slightly, about as long as a human might. And if you give Spaun a long list of numbers to remember, it has an easier time recalling the ones it received first and last, but struggles a bit to remember the ones in the middle.

“There are some fairly subtle details of human behavior that the model does capture,” says Chris Eliasmith, Spaun’s chief inventor. “It’s definitely not on the same scale. But it gives a flavor of a lot of different things brains can do.”

Brain drains

The fact that Spaun can move from one task to another brings us one step closer to being able to understand how our brains are able to shift so effortlessly from reading a note to memorizing a phone number to telling our hand to open a door.

And that could help scientists equip robots with the ability to be more flexible thinkers, to adjust on the fly. Also, because Spaun operates more like a human brain, researchers could use it to run health experiments that they couldn’t do on humans.

Recently, for instance, Eliasmith ran a test in which he killed off the neurons in a brain model at the same rate that neurons die in people as they age. He wanted to see how the loss of neurons affected the model’s performance on an intelligence test.

One thing Eliasmith hasn’t been able to do is to get Spaun to recognize if it’s doing a good or a bad job. He’s working on it.