It's frustrating to have a clear mental image of something but not be able to exactly get it across in words or a drawing. Now, a team of neuroscientists from the University of Toronto Scarborough has developed a way to digitally recreate exactly the image someone is thinking about, by scanning their brain.
So-called mind-reading technology is getting eerily accurate. Along with allowing people to control prosthetics with their thoughts, these systems have quickly advanced from picking out what number you're thinking of to decoding more complex concepts. It's all happening so fast that some researchers have proposed new human rights regarding how the brain can be read or manipulated.
The new study was designed to see whether specific images could be plucked out of a person's mind. To test out the idea, the team hooked people up to electroencephalography (EEG) equipment and then showed them pictures of faces on a computer screen. The EEG system recorded their brain waves, and after running the data through machine learning algorithms the system was able to digitally recreate the face that the test subject had just seen.
"When we see something, our brain creates a mental percept, which is essentially a mental impression of that thing," says Dan Nemrodov, co-author of the study. "We were able to capture this percept using EEG to get a direct illustration of what's happening in the brain during this process."

If you've opted to go for low "mood lighting" in your office, you might want to think again. According to a new study from Michigan State University, when rats are exposed to dim lighting for prolonged periods, their brain capacity diminishes. The same could likely be true for humans.
The rodents used in the study were Nile grass rats, which are diurnal – that means they sleep at night and are active during the day.
When a group of the animals were exposed to dim light during the day for a period of four weeks, they lost about 30 percent of the capacity in their hippocampus, which is a part of the brain associated with learning and memory. They thus performed poorly on a spatial task that they had learned and performed previously.
Another group of rats had been exposed to bright light every day for four weeks, and their performance on that same task improved after that time. Additionally, the performance and brain capacity of the dim-light rats recovered fully, after they were given a break for one month and then subjected to four weeks of bright light.
It was found that sustained exposure to dim light led to a marked reduction in brain derived neurotrophic factor, which is a peptide that maintains healthy connections and neurons in the hippocampus. The dim light also caused a reduction in dendritic spines, which are the connections that allow neurons to communicate with one another.
Because light doesn't affect the hippocampus directly, the scientists believe that it must first be acting on other parts of the brain, after passing through the eyes. One area that's a possibility is a group of neurons within the hypothalamus, which produce a peptide known as orexin. That peptide, in turn, is known to influence a variety of brain functions.
Given this fact, the researchers wonder if giving orexin to the dim-light rats would have the same affect as exposing them to bright light. If it does, then it could have implications for people such as the elderly, or those who have eye problems.
"For people with eye disease who don't receive much light, can we directly manipulate this group of neurons in the brain, bypassing the eye, and provide them with the same benefits of bright light exposure?" asks Lily Yan, who worked on the project along with Antonio "Tony" Nunez and Joel Soler. "Another possibility is improving the cognitive function in the aging population and those with neurological disorders. Can we help them recover from the impairment or prevent further decline?".

Right now, you're carrying around the most powerful computer in existence – the human brain. This naturally super-efficient machine is far better than anything humans have ever built, so it's not surprising that scientists are trying to reverse-engineer it. Rather than binary bits of information, neuromorphic computers are built with networks of artificial neurons, and now an MIT team has developed a more lifelike synapse to better connect those neurons.
For simplicity's sake, computers process and store information in a binary manner – everything can be broken down into a series of ones and zeroes. This system has served us well for the better part of a century, but having access to a whole new world of analog "grey areas" in between could really give computing power a shot in the arm.
The brain is a perfect model for those kinds of systems. While we've barely scratched the surface of how it works exactly, what we do know is that the brain deals with both analog and digital signals, processes and stores information in the same regions, and performs many operations in parallel. This is thanks to around 100 billion neurons dynamically communicating with each other via some 100 trillion synapses.
While neural networks mimic human thinking on the software side, neuromorphic chips are much more brain-like in the design of their hardware. Their architecture is made up of artificial neurons that process data and communicate with each other through artificial synapses. IBM's TrueNorth supercomputer is one of the most powerful neuromorphic systems, and Intel has recently unveiled a more modest, research-focused chip it calls Loihi.
In conventional neuromorphic chips, synapses are made of amorphous materials wedged in between the conductive layers of neighboring neurons. Ions flow through this material when a voltage is applied, transferring data between neurons. The problem is they can be unpredictable, with defects in the switching medium sending ions wandering off in different directions.

 "Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way," says Jeehwan Kim, lead researcher on the project. "But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects. This stream is changing, and it's hard to control. That's the biggest problem – nonuniformity of the artificial synapse."
To combat the problem, the MIT researchers designed a new medium for an artificial synapse. They started with a wafer of single-crystalline silicon, then grew a layer of silicon germanium over the top. Both materials have a lattice-like pattern, but silicon germanium's pattern is slightly larger, so when the two overlap it forms a kind of funnel shape, keeping ions on the straight and narrow.
The team built a neuromorphic chip using this technique, with silicon germanium synapses measuring about 25 nanometers wide. The team then tested them all by applying a voltage to them, and found that overall there was about a four percent variation in the current that passed through them. An individual synapse, tested over 700 cycles, was also found to keep a consistent current, with a variation of just 1 percent.
"This is the most uniform device we could achieve, which is the key to demonstrating artificial neural networks," says Kim.
The scientists then put the chip through its paces with a simulated test. They used an artificial neural network that functioned as though it was made up of three sheets of the neurons connected with two layers of the synapses. Then they fed in data on tens of thousands of handwriting samples, and found that the system was later able to recognize 95 percent of samples it was then given. That's not far off the 97 percent accuracy that more established systems can achieve.
Next, the team plans to develop a physical neuromorphic chip that can handle this task in the real world.
"Ultimately we want a chip as big as a fingernail to replace one big supercomputer," says Kim. "This opens a stepping stone to produce real artificial hardware."