Our Brains keeps Us 15 Seconds in the Past

Our eyes are continuously bombarded by an enormous amount of visual information – millions of shapes, colors, and ever-changing motion all around us.
For the brain, this is no easy feat.
On the one hand, the visual world alters continuously because of changes in light, viewpoint, and other factors. On the other, our visual input constantly changes due to blinking and the fact that our eyes, head, and body are frequently in motion.

To get an idea of the "noisiness" of this visual input, place a phone in front of your eyes and record a live video while you are walking around and looking at different things.
The jittery, messy result is exactly what your brain deals with in every moment of your visual experience.

Yet, seeing never feels like work for us. Rather than perceiving the fluctuations and visual noise that a video might record, we perceive a consistently stable environment.
So how does our brain create this illusion of stability? This process has fascinated scientists for centuries and it is one of the fundamental questions in vision science.

The time machine brain

In our latest research, we discovered a new mechanism that, among others, can explain this illusory stability.

The brain automatically smoothes our visual input over time. Instead of analyzing every single visual snapshot, we perceive in a given moment an average of what we saw in the past 15 seconds. So, by pulling together objects to appear more similar to each other, our brain tricks us into perceiving a stable environment.

Living "in the past" can explain why we do not notice subtle changes that occur over time.
In other words, the brain is like a time machine which keeps sending us back in time. It's like an app that consolidates our visual input every 15 seconds into one impression so that we can handle everyday life.
If our brains were always updating in real time, the world would feel like a chaotic place with constant fluctuations in light, shadow, and movement. We would feel like we were hallucinating all the time.

We created an illusion to illustrate how this stabilization mechanism works.

Instead of seeing the latest image in real time, humans actually see earlier versions because our brain's refresh time is about 15 seconds. So this illusion demonstrates that visual smoothing over time can help stabilize perception.

What the brain is essentially doing is procrastinating. It's too much work to constantly deal with every single snapshot it receives, so the brain sticks to the past because the past is a good predictor of the present.

Basically, we recycle information from the past because it's more efficient, faster, and less work.

This idea – which is also supported by other results – of mechanisms within the brain that continuously bias our visual perception towards our past visual experience is known as continuity fields.

Our visual system sometimes sacrifices accuracy for the sake of a smooth visual experience of the world around us. This can explain why, for example, when watching a film we don't notice subtle changes that occur over time, such as the difference between actors and their stunt doubles.

Read more at Science Alert 

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Merry Christmas

 

The Universe never ceases to surprise us.

We know eye-shaped nebulae (Helix Nebula), "hand of god" shape and other beautiful shapes, now NASA reveals a "Christmas Tree" nebula, whose technical astronomical name is the cluster NGC 2264.

This collection of stars and gas is between one and five million years old.

NGC 2264 is, in fact, a cluster of young stars - aged between one and five million years - in our Milky Way Galaxy, around 2500 light-years from Earth. The stars in NGC 2264 are both smaller and larger than the Sun, ranging from some with less than a tenth of the Sun's mass to others with around seven solar masses.

This new composite image highlights the resemblance to a Christmas tree through color choices and rotation. The blue and white lights (blinking in the animated version of this image) are young stars emitting x-rays detected by NASA's Chandra X-ray Observatory.

Optical data from the National Science Foundation's 0.9-meter WIYN telescope at Kitt Peak shows the gas in the nebula in green, corresponding to the tree's "pine needles," and infrared data from the Two Micron All Sky Survey shows the top stars. and background in white.

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A.I created 40,000 chemical weapons

 

Evil-oriented human intelligence and A.I can destroy humanity.

Take note: during a conference on unconventional weapons, researchers from the company “Collaboration Pharmaceuticals” showed an experiment where artificial intelligence machine learning technology created formulas for 40,000 biological weapons.

The experience was intended to show what A.I would be capable of and also to show that this resource, in the hands of dangerous groups and without supervision, would be treacherous.

In an interview given to "The Verge", Fabio Urbina, the study's primary author, spoke about how A.I managed to invent thousands of new substances - some frighteningly similar to the VX agent, an extremely powerful gas that attacks the nervous system of victims.

He explained that the study is a sort of “180° turn” from his normal work. On a daily basis, the scientist is tasked with researching machine learning models to discover new drugs and treatments.

However, it also involves implementing “evil” A.I models to ensure that any medication developed from their work does not have any toxic effects.

“For example”, he said, “imagine that you discover a wonderful pill that controls high blood pressure. But she does this by blocking some important channel connected to her heart. So this drug is automatically discarded because it is considered high risk.”.

The research was carried out at the invitation of the organization of the "Convergence" conference, held in Switzerland, and they asked that very technical information be kept secret for security reasons. What he told, however, traces an interesting procedural timeline:

“Basically, we have several historical databases on molecules tested for their toxicity or lack thereof,” Urbina said. "For this experiment, we focused on the molecular makeup of Agent VX, which acts as an inhibitor of something called 'Acetylcholinesterase'."

Acetylcholinesterase is, roughly speaking, an enzyme that acts in the transmission of information from the nervous system. When your brain gives an order, say, to bend your arm, this enzyme is what carries that command from point A to point B.

"The mortality of the VX lies in the fact that it prevents these commands from getting to where they are supposed to be if the order is anything muscle-related."

[VX] can stop your diaphragm or lung muscles, and your breathing literally stops and you suffocate.”

Based on this, Fabio Urbina and his team created a machine learning model that, roughly speaking, analyzed these databases, identified which parts of a molecule are toxic or not, and “learn” to glue molecules together, suggesting the creation of new chemical agents – this process uses an A.I either for good (the creation of new medicines) or for evil (the creation of chemical weapons and biological warfare agents).

So the team of scientists basically tweaked the A.I ​​to act like an “evil genius” and saw what it would do:

“We didn't know very well what would come out, since our capacity for generating models is formed by new technologies, that are not yet widely used”, explained Urbina.

“The first surprise was that many of the suggested compounds were far more toxic than VX, and that comes as a surprise because VX is one of the most toxic compounds out there, you need a very, very small dose to be lethal.”

A side note here: according to the US Center for Disease Control (CDC) page, VX is not “one of” the most lethal, but rather “the most” lethal of nerve agents.

The scientist explains that the models generated by the A.I ​​correspond to chemical weapons that have not been verified by the human hand – obviously, let’s face it – but normally these suggestions made by machine learning are quite solid. In other words, the error rate is low, and, given this perception, the application of this technology for the creation of lethal biological weapons is quite feasible.

The complete interview on the website "The Verge", gives other details, such as, for example, the fact that the machine learning model learned to create already known toxic compounds without ever having seen them in the database. Or yet, how this molecular model generation technology is so easily accessible that a simple Google search already puts anyone on the right path to program something like that (unfortunately).

Source: The Verge

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Living electrodes in Brain

 

It sounds like science fiction, but it isn't.

Living electrodes were placed in the brain of rats. The purpose of the experiments is to study how, in the future, to apply this technology to humans and to be able to connect the brain to a computer or any machine (CBI: Computer Brain Interface).

Dr Kacy Cullen, an associate professor of neurosurgery at the University of Pennsylvania, developed an electrode made of living tissue (tiny three-dimensional strands of brain cells).

Dr Cullen also has his own neurosurgery laboratory (the Cullen Lab).

Grown from stem cells and packaged in biodegradable gel tubes, these natural electrodes can be softer in the brain, fusing and connecting with living tissue rather than injuring it. He hopes they can solve some of the implant problems used in DBS and BCIs and potentially make these treatments available to more patients.

"This is an organic interface that really allows you to speak the language of the brain - with the added benefit of being self-renewing, as it is a living entity," said Cullen. Live electrodes can not only replace traditional electrodes, but also restore the "connectoma", the long-distance connections that can stretch and break during brain damage or wither due to diseases like Parkinson's or epilepsy.

Sources:

New Scientist , Neo Life, Bio Rxiv.

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