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The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn’t just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain.
That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain.
The key words here are “inspired by.” Despite Richards’ conviction, the odds were stacked against him. The human brain, as it happens, seems to lack a critical function that’s programmed into deep learning algorithms. On the surface, the algorithms were violating basic biological facts already proven by neuroscientists.
But what if, superficial differences aside, deep learning and the brain are actually compatible?
Now, in a new study published in eLife, Richards, working with DeepMind, proposed a new algorithm based on the biological structure of neurons in the neocortex. Also known as the cortex, this outermost region of the brain is home to higher cognitive functions such as reasoning, prediction, and flexible thought.
The team networked their artificial neurons together into a multi-layered network and challenged it with a classic computer vision task—identifying hand-written numbers.
The new algorithm performed well. But the kicker is that it analyzed the learning examples in a way that’s characteristic of deep learning algorithms, even though it was completely based on the brain’s fundamental biology.
“Deep learning is possible in a biological framework,” concludes the team.
Because the model is only a computer simulation at this point, Richards hopes to pass the baton to experimental neuroscientists, who could actively test whether the algorithm operates in an actual brain.
If so, the data could then be passed back to computer scientists to work out the next generation of massively parallel and low-energy algorithms to power our machines.
It’s a first step towards merging the two fields back into a “virtuous circle” of discovery and innovation.
The blame game
While you’ve probably heard of deep learning’s recent wins against humans in the game of Go, you might not know the nitty-gritty behind the algorithm’s operations.
In a nutshell, deep learning relies on an artificial neural network with virtual “neurons.” Like a towering skyscraper, the network is structured into hierarchies: lower-level neurons process aspects of an input—for example, a horizontal or vertical stroke that eventually forms the number four—whereas higher-level neurons extract more abstract aspects of the number four.
To teach the network, you give it examples of what you’re looking for. The signal propagates forward in the network (like climbing up a building), where each neuron works to fish out something fundamental about the number four.
Like children trying to learn a skill the first time, initially the network doesn’t do so well. It spits out what it thinks a universal number four should look like—think a Picasso-esque rendition.
But here’s where the learning occurs: the algorithm compares the output with the ideal output, and computes the difference between the two (dubbed “error”). This error is then “backpropagated” throughout the entire network, telling each neuron: hey, this is how far off you were, so try adjusting your computation closer to the ideal.
Millions of examples and tweakings later, the network inches closer to the desired output and becomes highly proficient at the trained task.
This error signal is crucial for learning. Without efficient “backprop,” the network doesn’t know which of its neurons are off kilter. By assigning blame, the AI can better itself.
The brain does this too. How? We have no clue.
What’s clear, though, is that the deep learning solution doesn’t work.
Backprop is a pretty needy function. It requires a very specific infrastructure for it to work as expected.
For one, each neuron in the network has to receive the error feedback. But in the brain, neurons are only connected to a few downstream partners (if that). For backprop to work in the brain, early-level neurons need to be able to receive information from billions of connections in their downstream circuits—a biological impossibility.
And while certain deep learning algorithms adapt a more local form of backprop— essentially between neurons—it requires their connection forwards and backwards to be symmetric. This hardly ever occurs in the brain’s synapses.
More recent algorithms adapt a slightly different strategy, in that they implement a separate feedback pathway that helps the neurons to figure out errors locally. While it’s more biologically plausible, the brain doesn’t have a separate computational network dedicated to the blame game.
What it does have are neurons with intricate structures, unlike the uniform “balls” that are currently applied in deep learning.
The team took inspiration from pyramidal cells that populate the human cortex.
“Most of these neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface,” says Richards. “What’s interesting is that these roots receive a different set of inputs than the branches that are way up at the top of the tree.”
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual cortex. Right: Illustration of simplified pyramidal neuron models. Image Credit: CIFAR
Curiously, the structure of neurons often turn out be “just right” for efficiently cracking a computational problem. Take the processing of sensations: the bottoms of pyramidal neurons are right smack where they need to be to receive sensory input, whereas the tops are conveniently placed to transmit feedback errors.
Could this intricate structure be evolution’s solution to channeling the error signal?
The team set up a multi-layered neural network based on previous algorithms. But rather than having uniform neurons, they gave those in middle layers—sandwiched between the input and output—compartments, just like real neurons.
When trained with hand-written digits, the algorithm performed much better than a single-layered network, despite lacking a way to perform classical backprop. The cell-like structure itself was sufficient to assign error: the error signals at one end of the neuron are naturally kept separate from input at the other end.
Then, at the right moment, the neuron brings both sources of information together to find the best solution.
There’s some biological evidence for this: neuroscientists have long known that the neuron’s input branches perform local computations, which can be integrated with signals that propagate backwards from the so-called output branch.
However, we don’t yet know if this is the brain’s way of dealing blame—a question that Richards urges neuroscientists to test out.
What’s more, the network parsed the problem in a way eerily similar to traditional deep learning algorithms: it took advantage of its multi-layered structure to extract progressively more abstract “ideas” about each number.
“[This is] the hallmark of deep learning,” the authors explain.
The Deep Learning Brain
Without doubt, there will be more twists and turns to the story as computer scientists incorporate more biological details into AI algorithms.
One aspect that Richards and team are already eyeing is a top-down predictive function, in which signals from higher levels directly influence how lower levels respond to input.
Feedback from upper levels doesn’t just provide error signals; it could also be nudging lower processing neurons towards a “better” activity pattern in real-time, says Richards.
The network doesn’t yet outperform other non-biologically derived (but “brain-inspired”) deep networks. But that’s not the point.
“Deep learning has had a huge impact on AI, but, to date, its impact on neuroscience has been limited,” the authors say.
Now neuroscientists have a lead they could experimentally test: that the structure of neurons underlie nature’s own deep learning algorithm.
“What we might see in the next decade or so is a real virtuous cycle of research between neuroscience and AI, where neuroscience discoveries help us to develop new AI and AI can help us interpret and understand our experimental data in neuroscience,” says Richards.
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Ask any neuroscientist to draw you a neuron, and it’ll probably look something like a star with two tails: one stubby with extensive tree-like branches, the other willowy, lengthy and dotted with spindly spikes.
While a decent abstraction, this cartoonish image hides the uncomfortable truth that scientists still don’t know much about what many neurons actually look like, not to mention the extent of their connections.
But without untangling the jumbled mess of neural wires that zigzag across the brain, scientists are stumped in trying to answer one of the most fundamental mysteries of the brain: how individual neuronal threads carry and assemble information, which forms the basis of our thoughts, memories, consciousness, and self.
What if there was a way to virtually trace and explore the brain’s serpentine fibers, much like the way Google Maps allows us to navigate the concrete tangles of our cities’ highways?
Thanks to an interdisciplinary team at Janelia Research Campus, we’re on our way. Meet MouseLight, the most extensive map of the mouse brain ever attempted. The ongoing project has an ambitious goal: reconstructing thousands—if not more—of the mouse’s 70 million neurons into a 3D map. (You can play with it here!)
With map in hand, neuroscientists around the world can begin to answer how neural circuits are organized in the brain, and how information flows from one neuron to another across brain regions and hemispheres.
The first release, presented Monday at the Society for Neuroscience Annual Conference in Washington, DC, contains information about the shape and sizes of 300 neurons.
And that’s just the beginning.
“MouseLight’s new dataset is the largest of its kind,” says Dr. Wyatt Korff, director of project teams. “It’s going to change the textbook view of neurons.”
MouseLight is hardly the first rodent brain atlasing project.
The Mouse Brain Connectivity Atlas at the Allen Institute for Brain Science in Seattle tracks neuron activity across small circuits in an effort to trace a mouse’s connectome—a complete atlas of how the firing of one neuron links to the next.
MICrONS (Machine Intelligence from Cortical Networks), the $100 million government-funded “moonshot” hopes to distill brain computation into algorithms for more powerful artificial intelligence. Its first step? Brain mapping.
What makes MouseLight stand out is its scope and level of detail.
MICrONS, for example, is focused on dissecting a cubic millimeter of the mouse visual processing center. In contrast, MouseLight involves tracing individual neurons across the entire brain.
And while connectomics outlines the major connections between brain regions, the birds-eye view entirely misses the intricacies of each individual neuron. This is where MouseLight steps in.
Slice and Dice
With a width only a fraction of a human hair, neuron projections are hard to capture in their native state. Tug or squeeze the brain too hard, and the long, delicate branches distort or even shred into bits.
In fact, previous attempts at trying to reconstruct neurons at this level of detail topped out at just a dozen, stymied by technological hiccups and sky-high costs.
A few years ago, the MouseLight team set out to automate the entire process, with a few time-saving tweaks. Here’s how it works.
After injecting a mouse with a virus that causes a handful of neurons to produce a green-glowing protein, the team treated the brain with a sugar alcohol solution. This step “clears” the brain, transforming the beige-colored organ to translucent, making it easier for light to penetrate and boosting the signal-to-background noise ratio. The brain is then glued onto a small pedestal and ready for imaging.
Building upon an established method called “two-photon microscopy,” the team then tweaked several parameters to reduce imaging time from days (or weeks) down to a fraction of that. Endearingly known as “2P” by the experts, this type of laser microscope zaps the tissue with just enough photos to light up a single plane without damaging the tissue—sharper plane, better focus, crisper image.
After taking an image, the setup activates its vibrating razor and shaves off the imaged section of the brain—a waspy slice about 200 micrometers thick. The process is repeated until the whole brain is imaged.
This setup increased imaging speed by 16 to 48 times faster than conventional microscopy, writes team leader Dr. Jayaram Chandrashekar, who published a version of the method early last year in eLife.
The resulting images strikingly highlight every crook and cranny of a neuronal branch, popping out against a pitch-black background. But pretty pictures come at a hefty data cost: each image takes up a whopping 20 terabytes of data—roughly the storage space of 4,000 DVDs, or 10,000 hours of movies.
Stitching individual images back into 3D is an image-processing nightmare. The MouseLight team used a combination of computational power and human prowess to complete this final step.
The reconstructed images are handed off to a mighty team of seven trained neuron trackers. With the help of tracing algorithms developed in-house and a keen eye, each member can track roughly a neuron a day—significantly less time than the week or so previously needed.
A Numbers Game
Even with just 300 fully reconstructed neurons, MouseLight has already revealed new secrets of the brain.
While it’s widely accepted that axons, the neurons’ outgoing projection, can span the entire length of the brain, these extra-long connections were considered relatively rare. (In fact, one previously discovered “giant neuron” was thought to link to consciousness because of its expansive connections).
Images captured from two-photon microscopy show an axon and dendrites protruding from a neuron’s cell body (sphere in center). Image Credit: Janelia Research Center, MouseLight project team
MouseLight blows that theory out of the water.
The data clearly shows that “giant neurons” are far more common than previously thought. For example, four neurons normally associated with taste had wiry branches that stretched all the way into brain areas that control movement and process touch.
“We knew that different regions of the brain talked to each other, but seeing it in 3D is different,” says Dr. Eve Marder at Brandeis University.
“The results are so stunning because they give you a really clear view of how the whole brain is connected.”
With a tested and true system in place, the team is now aiming to add 700 neurons to their collection within a year.
But appearance is only part of the story.
We can’t tell everything about a person simply by how they look. Neurons are the same: scientists can only infer so much about a neuron’s function by looking at their shape and positions. The team also hopes to profile the gene expression patterns of each neuron, which could provide more hints to their roles in the brain.
MouseLight essentially dissects the neural infrastructure that allows information traffic to flow through the brain. These anatomical highways are just the foundation. Just like Google Maps, roads form only the critical first layer of the map. Street view, traffic information and other add-ons come later for a complete look at cities in flux.
The same will happen for understanding our ever-changing brain.
Image Credit: Janelia Research Campus, MouseLight project team Continue reading
It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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When it comes to biomolecules, RNA doesn’t get a lot of love.
Maybe you haven’t even heard of the silent workhorse. RNA is the cell’s de facto translator: like a game of telephone, RNA takes DNA’s genetic code to a cellular factory called ribosomes. There, the cell makes proteins based on RNA’s message.
But RNA isn’t just a middleman. It controls what proteins are formed. Because proteins wiz around the cell completing all sorts of important processes, you can say that RNA is the gatekeeper: no RNA message, no proteins, no life.
In a new study published in Nature, RNA finally took center stage. By adding bits of genetic material to the E. Coli bacteria, a team of biohackers at the Wyss Institute hijacked the organism’s RNA messengers so that they only spring into action following certain inputs.
The result? A bacterial biocomputer capable of performing 12-input logic operations—AND, OR, and NOT—following specific inputs. Rather than outputting 0s and 1s, these biocircuits produce results based on the presence or absence of proteins and other molecules.
“It’s the greatest number of inputs in a circuit that a cell has been able to process,” says study author Dr. Alexander Green at Arizona State University. “To be able to analyze those signals and make a decision is the big advance here.”
When given a specific set of inputs, the bacteria spit out a protein that made them glow neon green under fluorescent light.
But synthetic biology promises far more than just a party trick—by tinkering with a cell’s RNA repertoire, scientists may one day coax them to photosynthesize, produce expensive drugs on the fly, or diagnose and hunt down rogue tumor cells.
Illustration of an RNA-based ‘ribocomputing’ device that makes logic-based decisions in living cells. The long gate RNA (blue) detects the binding of an input RNA (red). The ribosome (purple/mauve) reads the gate RNA to produce an output protein. Image Credit: Alexander Green / Arizona State University
The software of life
This isn’t the first time that scientists hijacked life’s algorithms to reprogram cells into nanocomputing systems. Previous work has already introduced to the world yeast cells that can make anti-malaria drugs from sugar or mammalian cells that can perform Boolean logic.
Yet circuits with multiple inputs and outputs remain hard to program. The reason is this: synthetic biologists have traditionally focused on snipping, fusing, or otherwise arranging a cell’s DNA to produce the outcomes they want.
But DNA is two steps removed from proteins, and tinkering with life’s code often leads to unexpected consequences. For one, the cell may not even accept and produce the extra bits of DNA code. For another, the added code, when transformed into proteins, may not act accordingly in the crowded and ever-changing environment of the cell.
What’s more, tinkering with one gene is often not enough to program an entirely new circuit. Scientists often need to amp up or shut down the activity of multiple genes, or multiple biological “modules” each made up of tens or hundreds of genes.
It’s like trying to fit new Lego pieces in a specific order into a room full of Lego constructions. Each new piece has the potential to wander off track and click onto something it’s not supposed to touch.
Getting every moving component to work in sync—as you might have guessed—is a giant headache.
The RNA way
With “ribocomputing,” Green and colleagues set off to tackle a main problem in synthetic biology: predictability.
Named after the “R (ribo)” in “RNA,” the method grew out of an idea that first struck Green back in 2012.
“The synthetic biological circuits to date have relied heavily on protein-based regulators that are difficult to scale up,” Green wrote at the time. We only have a limited handful of “designable parts” that work well, and these circuits require significant resources to encode and operate, he explains.
RNA, in comparison, is a lot more predictable. Like its more famous sibling DNA, RNA is composed of units that come in four different flavors: A, G, C, and U. Although RNA is only single-stranded, rather than the double helix for which DNA is known for, it can bind short DNA-like sequences in a very predictable manner: Gs always match up with Cs and As always with Us.
Because of this predictability, it’s possible to design RNA components that bind together perfectly. In other words, it reduces the chance that added RNA bits might go rogue in an unsuspecting cell.
Normally, once RNA is produced it immediately rushes to the ribosome—the cell’s protein-building factory. Think of it as a constantly “on” system.
However, Green and his team found a clever mechanism to slow them down. Dubbed the “toehold switch,” it works like this: the artificial RNA component is first incorporated into a chain of A, G, C, and U folded into a paperclip-like structure.
This blocks the RNA from accessing the ribosome. Because one RNA strand generally maps to one protein, the switch prevents that protein from ever getting made.
In this way, the switch is set to “off” by default—a “NOT” gate, in Boolean logic.
To activate the switch, the cell needs another component: a “trigger RNA,” which binds to the RNA toehold switch. This flips it on: the RNA grabs onto the ribosome, and bam—proteins.
String a few RNA switches together, with the activity of each one relying on the one before, and it forms an “AND” gate. Alternatively, if the activity of each switch is independent, that’s an “OR” gate.
“Basically, the toehold switches performed so well that we wanted to find a way to best exploit them for cellular applications,” says Green. They’re “kind of the equivalent of your first transistors,” he adds.
Once the team optimized the designs for different logic gates, they carefully condensed the switches into “gate RNA” molecules. These gate RNAs contain both codes for proteins and the logic operations needed to kickstart the process—a molecular logic circuit, so to speak.
If you’ve ever played around with an Arduino-controlled electrical circuit, you probably know the easiest way to test its function is with a light bulb.
That’s what the team did here, though with a biological bulb: green fluorescent protein, a light-sensing protein not normally present in bacteria that—when turned on—makes the microbugs glow neon green.
In a series of experiments, Green and his team genetically inserted gate RNAs into bacteria. Then, depending on the type of logical function, they added different combinations of trigger RNAs—the inputs.
When the input RNA matched up with its corresponding gate RNA, it flipped on the switch, causing the cell to light up.
Their most complex circuit contained five AND gates, five OR gates, and two NOTs—a 12-input ribocomputer that functioned exactly as designed.
That’s quite the achievement. “Everything is interacting with everything else and there are a million ways those interactions could flip the switch on accident,” says RNA researcher Dr. Julies Lucks at Northwestern University.
The specificity is thanks to RNA, the authors explain. Because RNAs bind to others so predictably, we can now design massive libraries of gate and trigger units to mix-and-match into all types of nano-biocomputers.
Although the technology doesn’t have any immediate applications, the team has high hopes.
For the first time, it’s now possible to massively scale up the process of programming new circuits into living cells. We’ve expanded the library of available biocomponents that can be used to reprogram life’s basic code, the authors say.
What’s more, when freeze-dried onto a piece of tissue paper, RNA keeps very well. We could potentially print RNA toehold switches onto paper that respond to viruses or to tumor cells, the authors say, essentially transforming the technology into highly accurate diagnostic platforms.
But Green’s hopes are even wilder for his RNA-based circuits.
“Because we’re using RNA, a universal molecule of life, we know these interactions can also work in other cells, so our method provides a general strategy that could be ported to other organisms,” he says.
Ultimately, the hope is to program neural network-like capabilities into the body’s other cells.
Imagine cells endowed with circuits capable of performing the kinds of computation the brain does, the authors say.
Perhaps one day, synthetic biology will transform our own cells into fully programmable entities, turning us all into biological cyborgs from the inside. How wild would that be?
Image Credit: Wyss Institute at Harvard University Continue reading