Summary of “Quanta Magazine”

They’re creating a single mathematical model that unites years of biological experiments and explains how the brain produces elaborate visual reproductions of the world based on scant visual information.
They’ve explained how neurons in the visual cortex interact to detect the edges of objects and changes in contrast, and now they’re working on explaining how the brain perceives the direction in which objects are moving.
Previous efforts to model human vision made wishful assumptions about the architecture of the visual cortex.
The retina is connected to the visual cortex, the part of the brain in the back of the head. However, there’s very little connectivity between the retina and the visual cortex.
For a visual area roughly one-quarter the size of a full moon, there are only about 10 nerve cells connecting the retina to the visual cortex.
LGN cells send a pulse to the visual cortex when they detect a change from dark to light, or vice versa, in their tiny section of the visual field.
For every 10 LGN neurons that snake back from the retina, there are 4,000 neurons in just the initial “Input layer” of the visual cortex – and many more in the rest of it.
All previous efforts assumed that more information travels between the retina and the cortex – an assumption that would make the visual cortex’s response to stimuli easier to explain.

The orginal article.

Summary of “A Magician Explains Why We See What’s Not There”

A magician sat at a table in front of a group of schoolchildren and threw a ball up in the air a few times.
Before the final throw, his hand secretly went under the table, letting the ball fall onto his lap, after which he proceeded to throw an imaginary ball up in the air.
Triplett’s Vanishing Ball Illusion relies on a principle that I often used to vanish objects, so I had some ideas as to why the illusion worked.
Importantly my gaze followed the ball, and in the pretend throw, my gaze followed the imaginary trajectory of the ball.
The findings from this study were surprising: In the normal version of the trick, nearly two-thirds of our adult participants experienced the illusion and claimed that they had seen the magician throw the ball up in the air and that it had left the screen at the top.
My intuition about the role of my gaze was correct, for the illusion was far less effective when I looked at my hand that was concealing the ball.
More recently, we have shown that even when you simply pretend to throw a ball up in the air without ever having thrown the ball for real, more than a third of people still experience the illusion.
During the fake throw, participants claimed to have seen the illusory ball at the top of the screen, but they did not move their eyes there, which suggests that our eyes are resilient to the illusion.

The orginal article.

Summary of “A Math Theory for Why People Hallucinate”

In a seminal 1979 paper, Cowan and his graduate student Bard Ermentrout reported that the electrical activity of neurons in the first layer of the visual cortex could be directly translated into the geometric shapes people typically see when under the influence of psychedelics.
These signals travel to the brain and stimulate neurons in the visual cortex in patterns that, under normal circumstances, mimic the patterns of light reflecting off objects in your field of view.
Sometimes patterns can arise spontaneously from the random firing of neurons in the cortex – internal background noise, as opposed to external stimuli – or when a psychoactive drug or other influencing factor disrupts normal brain function and boosts the random firing of neurons.
Activator neurons encourage nearby cells to also fire, amplifying electrical signals, while inhibitory neurons shut down their nearest neighbors, dampening signals.
The researchers noticed that activator neurons in the visual cortex were mostly connected to nearby activator neurons, while inhibitory neurons tended to connect to inhibitory neurons farther away, forming a wider network.
While Cowan recognized that there could be some kind of Turing mechanism at work in the visual cortex, his model didn’t account for noise – the random, bursty firing of neurons – which seemed likely to interfere with the formation of Turing patterns.
If an activator neuron randomly switches on, it can cause other nearby neurons to also switch on.
Whereas short-range connections between excitatory neurons are common, long-range connections between inhibitory neurons are sparse, and Goldenfeld thinks this helps suppress the spread of random signals.

The orginal article.

Summary of “Beyond Machine Sight”

Humans train computers to recognize specific content by “Showing” them a glut of images that both do and do not include the item the programmer wants the computer to recognize.
“Computer vision,” then, mislabels the agency and the process, a verbal misdirection that obscures serious problems.
Labeling facial recognition a type of “Computer vision” dangerously implies a level of nonhuman neutrality and insight that the software does not posses.
“Computer vision” is verbal misdirection, mislabeling the agency and the process.
While human visual standards still judge the success of this training, the process is more intrinsic to the machine than shallower computer vision techniques.
One of the reasons Li valorizes computer vision is in the hopes that it will be able to see what humans are not capable of seeing in their totality: the videos and pictures that make up 85 percent of internet content, what she calls “The dark matter of the digital age.” No human could physically “See,” much less understand, all those images, but a well-trained machine potentially could.
The New Scientist article on the Cambridge study described computers “Seeing through” disguises, but the tactic also brings to mind a blind person running their hands over a loved one’s face.
If we mainly focus on computer vision, we may be numbed into viewing our tactile relationship with machines as simple and self-evident rather than fraught and entangled.

The orginal article.

Summary of “When you split the brain, do you split the person?”

In so-called ‘split-brain’ patients, the corpus callosum – the highway for communication between the left and the right cerebral hemispheres – is surgically severed to halt otherwise intractable epilepsy.
What happens to the person? If the parts are no longer synchronised, does the brain still produce one person? The neuroscientists Roger Sperry and Michael Gazzaniga set out to investigate this issue in the 1960s and ’70s, and found astonishing data suggesting that when you split the brain, you split the person as well.
If you fixated on one point, then everything to the left of that point was processed by the right hemisphere, and everything to the right of your fixation point was processed by the left hemisphere.
The left hemisphere controlled the right side of the body and language output, while the right hemisphere controlled the left side of the body.
If you split the person when you split the brain, that leaves little room for an immaterial soul.
To try to get to the bottom of things, my team at the University of Amsterdam re-visited this fundamental issue by testing two split-brain patients, evaluating whether they could respond accurately to objects in the left visual field while also responding verbally or with the right hand.
When a stimulus appeared in the left visual field, the patient was better at indicating its visual properties, and when a stimulus appeared in the right visual field, he was better at verbally labelling it.
You split the brain into two halves, and yet you still have only one person.

The orginal article.