Ding DONG, open the door.
It’s me, Jake Roper.
Wow. Wow what a funny excellent regular joke.
But ya know, I bet you're thinking, I’ve heard regular jokes, tellmedadjokes.com which
is a cringey DONG according to Hannah. I love dad jokes. Those are something you can do online now guys...DONGs...not jokes.
Genes to Cognition Online features an interactive network map of cognitive disorders, cognitive
processes, and research approaches.
Cognitive disorders can be caused for a number of reasons including genes, head injuries,
or brain tumors to name a few.
Knowing someone that suffers from such a disorder can be particularly distressing when they
appear to be completely fine on the outside.
Alzheimer’s is one of these diseases in which the person afflicted can often appear
physically normal.
If we select Alzheimer’s disease we can see all the different color-coded elements
that can contribute to it.
The biochemistry of this disease is thought to be one of the most important elements yet
the least understood so let’s take a look.
There are videos and blurbs about it.
This specific one explains that a lack of serotonin, a characteristic of Alzheimer’s
contributes to acts of aggression and impulsivity.
It is a neurodegenerative disease meaning it’s characterized by the degeneration and
death of neurons.
But to see how a healthy neural circuit functions check out
Virtual Neurons.
The goal of this game is to construct a neural circuit that carries a message from skin cells
to muscle.
Once you construct it you can explore.
Zoom in and click where the circles overlap to see a pop up animation of synaptic transmission.
This gap between the neurons is the synaptic cleft.
In reality it’s quite a bit smaller than depicted here at only 20 to 40 nanometers
wide.
To put things in perspective a human hair is no less than 80,000 nanometers wide.
When this neuron is excited by an electrical signal called an action potential these vesicles
release chemicals called neurotransmitters into the synaptic cleft.
The neurotransmitters then react with the receptors on this neuron which in turn can
increase the chance that the neuron will fire an action potential and carry on to the next
cell.
Go back to the simulation to see this at work as this nail stabs the skin.
And since it’s no skin of your back to go to
Inner Animal you might as well.
When this person walks up you can move the slider to see it in its entirety.
Click on one of the glowing orbs to learn about that specific body part and how it evolved
from our ancestors.
This specific video explains why it hurts so much to land on your tailbone.
Because that is where our tail would have been, there isn’t much fat or muscle to
cover it up so...it hurts.
When you think of evolution you might mostly picture our ape-like ancestors but what about
fish?
An early human embryo looks very similar to that of mammals, birds, and amphibians, all
of which descended from fish.
As it grows and changes these features form into what we look like at birth.
In fact that little groove above your lip called a philtrum is one indication that we
descended from fish.
Now go back to your embryonic state and swim on over to
Avseoul.net/ParticleEqualizer instead.
It’s an audio visualization that moves to the sound of anything you want.
Beep boop bop
If you just want to see it in a dark abyss unselect this option.
And re-select
Whichever of these you have completed on Life Checklist, a webpage that keeps your data
so you can go back every time you’ve finished something new.
Finish middle school?
CHECK.
See the ocean.
Check
Get kissed? *awkward stuttering*
Like I said it saves your progress so you can come back to it but in the meantime
head to
Pixel chart, which is a dong inspired by this reddit post on gaussian distribution.
It takes pictures and breaks it down into thousands of particles.
They are then arranged by color intensities on a histogram.
Let’s give it ago by selecting a random image.
WHOA that looks cool.
Slow it down or speed it up.
The real cool part is right down here so check that out.
As you can see the largest number of pixels fall into this color space.
Looking at the histogram we can see that it matches.
Very few fall in the lightest and darkest ranges.
Now that we’re thinking about colors let’s play
Shape Mania.
The goal is to get the highest score possible like a lot of games.
But it’s fun and addictive.
Drag a dot to an empty star of the corresponding color and you will get one point.
Keep doing this and when there is a row of filled in stars they will empty and become
new stars.
Eventually you won’t be able to make any more moves but that’s okay because we have
internet slang.
Because we're cool and hip. Swaggy. Swag swag swaggy.
Type in whatever you want.
Like Hi is actually wat up. Cool.
And that’s great is that’s gr8 with an 8.
And ya know what else is gr8?
The sponsor of this episode, the wonderful Brilliant.org.
It’s a site full of lessons and practice quizzes in math and science subjects.
Now that we’ve constructed a neural network at the beginning of this DONG let’s check
out the lesson Artificial Neural Networks.
How can a computer distinguish pictures of dogs and cats?
Well it might be closer to how humans do than you think.
Let’s go through a problem.
When building a supervised learning model to distinguish whether an image is of a dog
or a cat, what should the inputs of the examples be?
Numerical data representing images of dogs and cats...yes! We did it!
Only 77% of people got it...which is a lot but I’m still proud of us because we did it together.
If you too want to be proud of all the questions you’ll get right or the wrong ones you’ll
learn from use the link in the description below for 20% off an annual premium subscription.
This offer is open for the first 36 of you DONG gang...DONGers. To click it so
just go down there do that. That's all I got for ya right now.
I love you and respect you and I am grateful for every moment we get to spend
together. I hope you have a wonderful life. I hope that you are as beautiful as you are always were and are.
And long live the empire and as always, thanks for watching.