The Neuron – A Hackers Perspective

It’s not too often that you see handkerchiefs around anymore. Today, they’re largely viewed as unsanitary and well… just plain gross. You’ll be quite disappointed to learn that they have absolutely nothing to do with this article other than a couple of similarities they share when compared to your neocortex. If you were to pull the neocortex from your brain and stretch it out on a table, you most likely wouldn’t be able to see that not only is it roughly the size of a large handkerchief; it also shares the same thickness.

The neocortex, or cortex for short, is Latin for “new rind”, or “new bark”, and represents the most recent evolutionary change to the mammalian brain. It envelopes the “old brain” and has several ridges and valleys (called sulci and gyri) that formed from evolution’s mostly successful attempt to stuff as much cortex as possible into our skulls. It has taken on the duties of processing sensory inputs and storing memories, and rightfully so. Draw a one millimeter square on your handkerchief cortex, and it would contain around 100,000 neurons. It has been estimated that the typical human cortex contains some 30 billion total neurons. If we make the conservative guess that each neuron has 1,000 synapses, that would put the total synaptic connections in your cortex at 30 trillion — a number so large that it is literally beyond our ability to comprehend. And apparently enough to store all the memories of a lifetime.

In the theater of your mind, imagine a stretched-out handkerchief lying in front of you. It is… you. It contains everything about you. Every memory that you have is in there. Your best friend’s voice, the smell of your favorite food, the song you heard on the radio this morning, that feeling you get when your kids tell you they love you is all in there. Your cortex, that little insignificant looking handkerchief in front of you, is reading this article at this very moment.

What an amazing machine; a machine that is made possible with a special type of cell – a cell we call a neuron. In this article, we’re going to explore how a neuron works from an electrical vantage point. That is, how electrical signals move from neuron to neuron and create who we are.

A Basic Neuron

Neuron diagram via Enchanted Learning

Despite the amazing feats a human brain performs, the neuron is comparatively simple when observed by itself. Neurons are living cells, however, and have many of the same complexities as other cells – such as a nucleus, mitochondria, ribosomes, and so on. Each one of these cellular parts could be the subject of an entire book. Its simplicity arises from the basic job it does – which is outputting a voltage when the sum of its inputs reaches a certain threshold, which is roughly 55 mV.

Using the image above, let’s examine the three major components of a neuron.


The soma is the cell body and contains the nucleus and other components of a typical cell. There are different types of neurons whose differing characteristics come from the soma. Its size can range from 4 to over 100 micrometers.


Dendrites protrude from the soma and act as the inputs of the neuron. A typical neuron will have thousands of dendrites, with each connecting to an axon of another neuron. The connection is called a synapse but is not a physical one. There is a gap between the ends of the dendrite and axon called a synaptic cleft. Information is relayed through the gap via neural transmitters, which are chemicals such as dopamine and serotonin.


Each neuron has only a single axon that extends from the soma, and acts similar to an electrical wire. Each axon will terminate with terminal fibers, forming synapses with as many as 1,000 other neurons. Axons vary in length and can reach a few meters long. The longest axons in the human body run from the bottom of the foot to the spinal cord.

The basic electrical operation of a neuron is to output a voltage spike from its axon when the sum of its input voltages (via its dendrites) crosses a specific threshold. And since axons are connected to dendrites of other neurons, you end up with this vastly complicated neural network.

Since we’re all a bunch of electronic types here, you might be thinking of these ‘voltage spikes’ as a difference of potential. But that’s not how it works. Not in the brain anyway. Let’s take a closer look at how electricity flows from neuron to neuron.

Action Potentials – The Communication Protocol of the Brain

The axon is covered in a myelin sheet which acts as an insulator. There are small breaks in the sheet along the length of the axon which are named after its discoverer, called Nodes of Ranvier. It’s important to note that these nodes are ion channels. In the spaces just outside and inside of the axon membrane exists a concentration of potassium and sodium ions. The ion channels will open and close, creating a local difference in the concentration of sodium and potassium ions.

Diagram via Washington U.

We all should know that an ion is an atom with a charge. In a resting state, the sodium/potassium ion concentration creates a negative 70 mV difference of potential between the outside and inside of the axon membrane, with there being a higher concentration of sodium ions outside and a higher concentration of potassium ions inside. The soma will create an action potential when -55 mV is reached. When this happens, a sodium ion channel will open. This lets positive sodium ions from outside the axon membrane to leak inside, changing the sodium/potassium ion concentration inside the axon, which in turn changes the difference of potential from -55 mV to around +40 mV. This process in known as depolarization.

Graph via Washington U.

One by one, sodium ion channels open along the entire length of the axon. Each one opens only for a short time, and immediately afterward, potassium ion channels open, allowing positive potassium ions to move from inside the axon membrane to the outside. This changes the concentration of sodium/potassium ions and brings the difference of potential back to its resting place of -70 mV in a process known as repolarization. Fro start to finish, the process takes about five milliseconds to complete. The process causes a 110 mV voltage spike to ride down the length of the entire axon, and is called an action potential. This voltage spike will end up in the soma of another neuron. If that particular neuron gets enough of these spikes, it too will create an action potential. This is the basic process of how electrical patterns propagate throughout the cortex.

The mammalian brain, specifically the cortex, is an incredible machine and capable of far more than even our most powerful computers. Understanding how it works will give us a better insight into building intelligent machines. And now that you know the basic electrical properties of a neuron, you’re in a better position to understand artificial neural networks.


Action Potential in Neurons, via Youtube

On Intelligence, by Jeff Hawkins, ISDN 978-0805078534

Wrap Your Mind Around Neural Networks

Artificial Intelligence is playing an ever increasing role in the lives of civilized nations, though most citizens probably don’t realize it. It’s now commonplace to speak with a computer when calling a business. Facebook is becoming scary accurate at recognizing faces in uploaded photos. Physical interaction with smart phones is becoming a thing of the past… with Apple’s Siri and Google Speech, it’s slowly but surely becoming easier to simply talk to your phone and tell it what to do than typing or touching an icon. Try this if you haven’t before — if you have an Android phone, say “OK Google”, followed by “Lumos”. It’s magic!

Advertisements for products we’re interested in pop up on our social media accounts as if something is reading our minds. Truth is, something is reading our minds… though it’s hard to pin down exactly what that something is. An advertisement might pop up for something that we want, even though we never realized we wanted it until we see it. This is not coincidental, but stems from an AI algorithm.

At the heart of many of these AI applications lies a process known as Deep Learning. There has been a lot of talk about Deep Learning lately, not only here on Hackaday, but all over the interwebs. And like most things related to AI, it can be a bit complicated and difficult to understand without a strong background in computer science.

If you’re familiar with my quantum theory articles, you’ll know that I like to take complicated subjects, strip away the complication the best I can and explain it in a way that anyone can understand. It is the goal of this article to apply a similar approach to this idea of Deep Learning. If neural networks make you cross-eyed and machine learning gives you nightmares, read on. You’ll see that “Deep Learning” sounds like a daunting subject, but is really just a $20 term used to describe something whose underpinnings are relatively simple.

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Keep an Eye on the Sky With rDuinoScope

We’ve all enjoyed looking up at a clear night sky and marveled at the majesty of the stars. Some of us have even pointed telescopes at particular celestial objects to get a closer view. Anyone who’s ever looked at anything beyond Jupiter knows the hassle involved.  It is most unfortunate that the planet we reside on happens to rotate about a fixed axis, which makes it somewhat difficult to keep a celestial object in the view of your scope.

It doesn’t take much to strap a few steppers and some silicon brains to a scope to counter the rotation of earth, and such systems have been available for decades. They are unfortunately quite expensive. So [Dessislav Gouzgounov] took matters into his own hands and developed the rDuinoScope – an open source telescope control system.

Based on the Arduino Due, the systems stores a database of 250 stellar objects. Combined with an RTC and GPS, the rDunioScope can locate and lock on to your favorite nebula and track it, allowing you to view it in peace. Be sure to grab the code and let us know when you have your own rDuinoScope set up!


Hanging 3D Printer Uses Entire Room As Print Bed

There are many things people do with spare rooms. Some make guest rooms, others make baby rooms, while a few even make craft rooms. What do hackers do with spare rooms? Turn them into giant 3D printers of course. [Torbjørn Ludvigsen] is a physics major out of Umea University in Sweden, and built the Hangprinter for only $250 in parts. It follows the RepRap tradition of being completely open source and made mostly from parts that it can print.

The printer is fully functional, proven by printing a five-foot tall model of the Tower of Babel. [Torbjorn] hopes to improve the printer to allow it to print pieces of furniture and other larger household items.

[Torbjorn] hopes that 3D printing will not go down the same road that 2D printing went, where the printers are designed to break after so many prints. Open source is the key to stopping such machines from getting out there.

Thanks to [Jeremy Southard] for the tip!

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Animated Picture Frame Needs Charging Once Per Month

[Kyle Stewart-Frantz] took one look at a black and white photo of a mountain stream, and decided it was way too boring. How much cooler would it be if the water was moving! Like any good hacker worth his weight in 2N2222s, [Kyle] set out to make his idea a reality. After discovering some pricey options, he found a Kindle Paperwhite with a display that had decent resolution and 16 levels of grey. But would 16 levels be sufficient to produce an animation that’s pleasing to the eye?

After stumbling upon a community dedicated to hacking Kindles, [Kyle] got to work. Using a custom Amazon command called eips, he was able to access the display’s memory location and paint images to it. The next trick was to write a script that called the command multiple times to produce a GIF-like animation effect.  This… didn’t work so well. He then found some code from [GeekMaster] (thanks for the tip!) that ran a specialized video player on the Kindle that used something called ordered dithering. After a few more tweaks, he got everything working and the end result looks like something straight out of the world of Harry Potter.

The animated picture frame can run for three to four weeks between charges. This is a hack that would make a great gift and look nice in your office. If you make one, be sure to put the skull and wrenches on it first and let us know!

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Flexible Quadcopter Is Nearly Indestructible

We’ve all crashed quadcopters. It’s almost inevitable. Everything is going along fine and dandy ’till mother nature opens her big mouth a blows a nasty gust of wind right at you, pushing your quad into the side of a wall. A wall that happens to be composed of a material that is quite a bit harder than your quadcopter. “What if…” you ask yourself while picking up the pieces of you shiny new quad off the ground… “they made these things out of flexible material?”

Well, it would appear someone has done just that. The crash resistant quadcopter is composed of a flexible frame (obviously) which is held rigid with magnets. So the frame works just like the frame of your average quad. Until you crash it, of course. Then it becomes flexible.

The idea came from the wing of a wasp, which you can apparently crumple without damaging it. Be sure to check out the video below of the drone showing off its flexible frame, and let us know if you’ve seen any other types of flexible frame drones in the wild.

Thanks to [JDHE] for the tip!

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Super Computing with Mini ITX Cluster

[Colin Alston] was able to snag a handful of Mini ITX motherboards for cheap and built a mini super computer he calls TinyJaguar. Named partly after the AMD Sempron 2650 APU, the TinyJaguar boasts four, yes that’s four MSI AM1I Mini-ITX motherboards, each with 4GB of DDR memory.

A Raspberry Pi with custom software manages the cluster, and along with some TTL and relays, controls the power to the four nodes. The mini super computer resides in a custom acrylic case held together by an array of 3D printed parts and fasteners.There’s even a rack-like faceplate near the bottom to host the RPi, an Ethernet switch, an array of status LEDs, and the two buttons.

With 16 total cores of computing power (including GPU), the TinyJaguar is quite capable of doing some pretty cool stuff such as running Jupyter notebook with IPyParallel. [Colin] ran into some issues getting the GPU to behave with PyOpenCL. It took a bit of pain and time, but in the end he was able to get the GPUs up, and wrote a small message passing program to show two of the cores were up and working together.

Be sure to check out [Colin’s] super computer project page, specifically the ten project logs that walk through everything that went into this build. He also posted his code if you want to take a look under the hood.