When auditory cells are modified to receive light, do you see sound, or hear light? To some trained gerbils at University Medical Center Göttingen, Germany under the care of [Tobias Moser], the question is moot. The gerbils were instructed to move to a different part of their cage when administrators played a sound, and when cochlear lights were activated on their modified cells, the gerbils obeyed their conditioning and went where they were supposed to go.
In the linked article, there is software which allows you to simulate what it is like to hear through a cochlear implant, or you can check out the video below the break which is not related to the article. Either way, improvements to the technology are welcome, and according to [Tobias]: “Optical stimulation may be the breakthrough to increase frequency resolution, and continue improving the cochlear implant”. The first cochlear implant was installed in 1964 so it has long history and a solid future.
This is not the only method for improving cochlear implants, and some don’t require any modified cells, but [Tobias] explained his reasoning. “I essentially took the harder route with optogenetics because it has a mechanism I understand,” and if that does not sound like so many hackers who reach for the tools they are familiar with, we don’t know what does. Revel in your Arduinos, 555 timers, transistors, or optogenetically modified cells, and know that your choice of tool is as powerful as the wielder.
Most posts here are electrical or mechanical, with a few scattered hacks from other fields. Those who also keep up with advances in biomedical research may have noticed certain areas are starting to parallel the electronics we know. [Dr. Rajib Shubert] is in one such field, and picked up on the commonality as well. He thought it’d be interesting to bridge the two worlds by explaining his research using analogies familiar to the Hackaday audience. (Video also embedded below.)
He laid the foundation with a little background, establishing that we’ve been able to see individual static neurons for a while via microscope slides and such, and we’ve been able to see activity of the whole living brain via functional MRI. These methods gradually improved our understanding of neurons, and advances within the past few years have reached an intersection of those two points: [Dr. Shubert] and colleagues now have tools to peer inside a functional brain, teasing out how it works one neuron at a time.
[Dr. Shubert]’s talk makes analogies to electronics hardware, but we can also make a software analogy treating the brain as a highly optimized (and/or obfuscated) piece of code. Virus stamping a single cell under this analogy is like isolating a single function, seeing who calls it, and who it calls. This pairs well with optogenetics techniques, which can be seen as like modifying a function to see how it affects results in real time. It certainly puts a different meaning on the phrase “working with live code”!
Cryonics — freezing humans for later revival — has been a staple of science fiction for ages. Maybe you want to be cured of something presently incurable or you just want to see the future. Of course, ignoring the problem of why anyone wants to thaw out a 500-year-old person, no one has a proven technology for thawing out one of these corpsicles. You are essentially betting that science will figure that out sometime before your freezer breaks down. A new startup called Nectome funded by Y Combinator wants to change your thinking about preservation. Instead of freezing they will pump you full of preservatives that preserve your brain including fine structures that scientists currently believe contain your memories.
Nectome’s strategy isn’t to have you revived like in conventional cryonics. They think the technology to do high definition scans of your preserved brain will exist soon. Those scans might allow future scientists to recreate your brain in a simulation. That isn’t really the same as coming back to life, though. At least we don’t imagine it is.
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
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.
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.
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.
It sounds like something out of a sci-fi or horror movie: people suffering from complete locked-in state (CLIS) have lost all motor control, but their brains are otherwise functioning normally. This can result from spinal cord injuries or anyotrophic lateral sclerosis (ALS). Patients who are only partially locked in can often blink to signal yes or no. CLIS patients don’t even have this option. So researchers are trying to literally read their minds.
Neuroelectrical technologies, like the EEG, haven’t been successful so far, so the scientists took another tack: using near-infrared light to detect the oxygenation of blood in the forehead. The results are promising, but we’re not there yet. The system detected answers correctly during training sessions about 70% of the time, where the upper bound for random chance is around 65% — varying from trial to trial. This may not seem overwhelmingly significant, but repeating the question many times can help improve confidence in the answer, and these are people with no means of communicating with the outside world. Anything is better than nothing?
It’s noteworthy that the blood oxygen curves over time vary significantly from patient to patient, but seem roughly consistent within a single patient. Some people simply have patterns that are easier to read. You can see all the data in the paper.
They go into the methodology as well, which is not straightforward either. How would you design a test for a person who you can’t even tell if they are awake, for instance? They ask complementary questions (“Paris is the capital of France”, “Berlin is the capital of Germany”, “Paris is the capital of Germany”, and “Berlin is the capital of France”) to be absolutely sure they’re getting the classifications right.
It’s interesting science, and for a good cause: improving the quality of life for people who have lost all contact with their bodies. (Most of whom answered “yes” to the statement “I am happy.” Food for thought.)
Most people wish they were more productive. Some buckle down and leverage some rare facet of their personality to force the work out. Some of them talk with friends. Some go on vision quests. There are lots of methods for lots of types of people. Most hackers, I’ve noticed, look for a datasheet. An engineer’s reference. We want to solve the problem like we solve technical problems.
There were three books that gave me the first hints at how to look objectively at my brain and start to hack on it a little. These were The Power of Habit by Charles Duhigg, Flow By Mihaly Csikszentmihalyi, and Getting Things Done By David Allen.
I sort of wandered into these books in a haphazard path. The first I encountered was The Power of Habit which I found to be a bit of a revelation. It presented the idea of habits as functions in the great computer program that makes up a person. The brain sees that you’re doing a task over and over again and just learns to do it. It keeps optimizing and optimizing this program over time. All a person needs to do is trigger the habit loop and then it will run.
For example: Typing. At first you either take a course or, if your parents left you alone with a computer for hours on end, hunt-and-peck your way to a decent typing speed. It involves a lot of looking down at the keyboard. Eventually you notice that you don’t actually need to look at the keyboard at all. Depending on your stage you may still be “t-h-i-n-k-i-n-g”, mentally placing each letter as you type. However, eventually your brain begins to abstract this away until it has stored, somewhere, a combination of hand movements for every single word or key combination you typically use. It’s only when you have to spell a new word that you fall back on older programs.
Evolution is one clever fellow. Next time you’re strolling about outdoors, pick up a pine cone and take a look at the layout of the bract scales. You’ll find an unmistakable geometric structure. In fact, this same structure can be seen in the petals of a rose, the seeds of a sunflower and even the cochlea bone in your inner ear. Look closely enough, and you’ll find this spiraling structure everywhere. It’s based on a series of integers called the Fibonacci sequence. Leonardo Bonacci discovered the sequence while trying to figure out how many rabbits he could make starting with just two. It’s quite simple — add the right most integer to the previous one to get the next one in the sequence. Starting from zero, this would give you 0-1-1-2-3-5-8-13-21 and so on. If one was to look at this sequence in the form of geometric shapes, they can create square tiles whose sides are the length of the value in the sequence. If you connect the diagonal corners of these tiles with an infinite curve, you end up with the spiral that you saw in the pine cone and other natural objects.
So how did mother nature discover this geometric structure? Surely it does not know math. How then can it come up with intricate and sophisticated structures? It turns out that this Fibonacci spiral is the most efficient way of squeezing the most amount of stuff in the least amount of space. And if one takes natural selection seriously, this makes perfect sense. Eons of trial and error to make the most copies of itself has stumbled upon a mathematical principle that permeates life on earth.
The homo sapiens brain is the product of this same evolutionary process, and has been evolving for an estimated 7 million years. It would be foolish to think that this same type of efficiency natural selection has stumbled across would not be present in the current homo sapiens brain. I want to impress upon you this idea of efficiency. Natural selection discovered the Fibonacci sequence solely because it is the most efficient way to do a particular task. If the brain has a task of storing information, it is perfectly reasonable that millions of years of evolution has honed it so that it does this in the most efficient way possible as well. In this article, we shall explore this idea of efficiency in data storage, and leave you to ponder its applications in the computer sciences.