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 “Dude, Where’s My Frontal Cortex?”

Around the onset of adolescence, the frontal cortex is the only brain region that has not reached adult levels of grey matter, made up of neuronal cell bodies.
The same plays out in the adolescent frontal cortex.
During risky decision-making, adolescents show less activation of some key sub-regions of the frontal cortex than do adults, and among adolescents, the less activity in these regions, the poorer the risk assessment.
It’s not the case that adolescents and adults have an equal desire to do the same dumb-ass thing, and the sole difference is that the fully mature frontal cortex in the latter prevents them from doing so.
Do the same with an adolescent and the frontal cortex remains silent and that agonized limbic network of teenage angst wails.
As my daughter began to crawl out, the teenagers in the audience did something you’re not supposed to do in a theater, something no adult with a developed frontal cortex would do.
In this view, wiring up something like the visual cortex can pretty much be wrapped up in the first year of life, but doing the same in the frontal cortex takes another quarter century.
The frontal cortex has the same basic structure as the rest of the cortex, uses the same neurotransmitters and types of neurons.

The orginal article.

Summary of “How the Brain Creates a Timeline of the Past”

These cells were each tuned to certain points in a span of time, with some firing, say, one second after a stimulus and others after five seconds, essentially bridging time gaps between experiences.
The rats seemed to be using these “Events” – changes in context – to get a sense of how much time had gone by.
“It shows how our perception of time is so elastic,” Shapiro said.
“A second can last forever. Days can vanish. It’s this coding by parsing episodes that, to me, makes a very neat explanation for the way we see time. We’re processing things that happen in sequences, and what happens in those sequences can determine the subjective estimate for how much time passes.” The researchers now want to learn just how that happens.
“We had these equations up on the board for the Laplace transform and the inverse around the same time people were discovering time cells. So we spent the last 10 years seeing the inverse, but we hadn’t seen the actual transform. Now we’ve got it. I’m pretty stoked.”
The discovery of time cells in those brain regions seems to support the idea.
Howard has also started to show that the same equations that the brain could use to represent time could also be applied to space, numerosity and decision-making based on collected evidence – really, to any variable that can be put into the language of these equations.
Of course, Howard’s model of how the brain represents time isn’t the only idea out there.

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.