Scientists have linked up two silicon-based artificial neurons with a biological one across multiple countries into a fully-functional network. Using standard internet protocols, they established a chain of communication whereby an artificial neuron controls a living, biological one, and passes on the info to another artificial one.
Whoa.
Weve talked plenty about brain-computer interfaces and novel computer chips that resemble the brain. Weve covered how those neuromorphic chips could link up into tremendously powerful computing entities, using engineered communication nodes called artificial synapses.
As Moores law is dying, we even said that neuromorphic computing is one path towards the future of extremely powerful, low energy consumption artificial neural network-based computingin hardwarethat could in theory better link up with the brain. Because the chips speak the brains language, in theory they could become neuroprosthesis hubs far more advanced and natural than anything currently possible.
This month, an international team put all of those ingredients together, turning theory into reality.
The three labs, scattered across Padova, Italy, Zurich, Switzerland, and Southampton, England, collaborated to create a fully self-controlled, hybrid artificial-biological neural network that communicated using biological principles, but over the internet.
The three-neuron network, linked through artificial synapses that emulate the real thing, was able to reproduce a classic neuroscience experiment thats considered the basis of learning and memory in the brain. In other words, artificial neuron and synapse chips have progressed to the point where they can actually use a biological neuron intermediary to form a circuit that, at least partially, behaves like the real thing.
Thats not to say cyborg brains are coming soon. The simulation only recreated a small network that supports excitatory transmission in the hippocampusa critical region that supports memoryand most brain functions require enormous cross-talk between numerous neurons and circuits. Nevertheless, the study is a jaw-dropping demonstration of how far weve come in recreating biological neurons and synapses in artificial hardware.
And perhaps one day, the currently experimental neuromorphic hardware will be integrated into broken biological neural circuits as bridges to restore movement, memory, personality, and even a sense of self.
One important thing: this study relies heavily on a decade of research into neuromorphic computing, or the implementation of brain functions inside computer chips.
The best-known example is perhaps IBMs TrueNorth, which leveraged the brains computational principles to build a completely different computer than what we have today. Todays computers run on a von Neumann architecture, in which memory and processing modules are physically separate. In contrast, the brains computing and memory are simultaneously achieved at synapses, small hubs on individual neurons that talk to adjacent ones.
Because memory and processing occur on the same site, biological neurons dont have to shuttle data back and forth between processing and storage compartments, massively reducing processing time and energy use. Whats more, a neurons history will also influence how it behaves in the future, increasing flexibility and adaptability compared to computers. With the rise of deep learning, which loosely mimics neural processing as the prima donna of AI, the need to reduce power while boosting speed and flexible learning is becoming ever more tantamount in the AI community.
Neuromorphic computing was partially born out of this need. Most chips utilize special ingredients that change their resistance (or other physical characteristics) to mimic how a neuron might adapt to stimulation. Some chips emulate a whole neuron, that is, how it responds to a history of stimulationdoes it get easier or harder to fire? Others imitate synapses themselves, that is, how easily they will pass on the information to another neuron.
Although single neuromorphic chips have proven to be far more efficient and powerful than current computer chips running machine learning algorithms in toy problems, so far few people have tried putting the artificial components together with biological ones in the ultimate test.
Thats what this study did.
Still with me? Lets talk network.
Its gonna sound complicated, but remember: learning is the formation of neural networks, and neurons that fire together wire together. To rephrase: when learning, neurons will spontaneously organize into networks so that future instances will re-trigger the entire network. To wire together, downstream neurons will become more responsive to their upstream neural partners, so that even a whisper will cause them to activate. In contrast, some types of stimulation will cause the downstream neuron to chill out so that only an upstream shout will trigger downstream activation.
Both these propertieseasier or harder to activate downstream neuronsare essentially how the brain forms connections. The amping up, in neuroscience jargon, is long-term potentiation (LTP), whereas the down-tuning is LTD (long-term depression). These two phenomena were first discovered in the rodent hippocampus more than half a century ago, and ever since have been considered as the biological basis of how the brain learns and remembers, and implicated in neurological problems such as addition (seriously, you cant pass Neuro 101 without learning about LTP and LTD!).
So its perhaps especially salient that one of the first artificial-brain hybrid networks recapitulated this classic result.
To visualize: the three-neuron network began in Switzerland, with an artificial neuron with the badass name of silicon spiking neuron. That neuron is linked to an artificial synapse, a memristor located in the UK, which is then linked to a biological rat neuron cultured in Italy. The rat neuron has a smart microelectrode, controlled by the artificial synapse, to stimulate it. This is the artificial-to-biological pathway.
Meanwhile, the rat neuron in Italy also has electrodes that listen in on its electrical signaling. This signaling is passed back to another artificial synapse in the UK, which is then used to control a second artificial neuron back in Switzerland. This is the biological-to-artificial pathway back. As a testimony in how far weve come in digitizing neural signaling, all of the biological neural responses are digitized and sent over the internet to control its far-out artificial partner.
Heres the crux: to demonstrate a functional neural network, just having the biological neuron passively pass on electrical stimulation isnt enough. It has to show the capacity to learn, that is, to be able to mimic the amping up and down-tuning that are LTP and LTD, respectively.
Youve probably guessed the results: certain stimulation patterns to the first artificial neuron in Switzerland changed how the artificial synapse in the UK operated. This, in turn, changed the stimulation to the biological neuron, so that it either amped up or toned down depending on the input.
Similarly, the response of the biological neuron altered the second artificial synapse, which then controlled the output of the second artificial neuron. Altogether, the biological and artificial components seamlessly linked up, over thousands of miles, into a functional neural circuit.
SoIm still picking my jaw up off the floor.
Its utterly insane seeing a classic neuroscience learning experiment repeated with an integrated network with artificial components. That said, a three-neuron network is far from the thousands of synapses (if not more) needed to truly re-establish a broken neural circuit in the hippocampus, which DARPA has been aiming to do. And LTP/LTD has come under fire recently as the de facto brain mechanism for learning, though so far they remain cemented as neuroscience dogma.
However, this is one of the few studies where you see fields coming together. As Richard Feynman famously said, What I cannot recreate, I cannot understand. Even though neuromorphic chips were built on a high-level rather than molecular-level understanding of how neurons work, the study shows that artificial versions can still synapse with their biological counterparts. Were not just on the right path towards understanding the brain, were recreating it, in hardwareif just a little.
While the study doesnt have immediate use cases, practically it does boost both the neuromorphic computing and neuroprosthetic fields.
We are very excited with this new development, said study author Dr. Themis Prodromakis at the University of Southampton. On one side it sets the basis for a novel scenario that was never encountered during natural evolution, where biological and artificial neurons are linked together and communicate across global networks; laying the foundations for the Internet of Neuro-electronics. On the other hand, it brings new prospects to neuroprosthetic technologies, paving the way towards research into replacing dysfunctional parts of the brain with AI chips.
Image Credit: Gerd Altmann from Pixabay
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Scientists Linked Artificial and Biological Neurons in a Networkand Amazingly, It Worked - Singularity Hub