Source: Girolamo Sferrazza Papa
Takeaway: Neurotechnologies and neural networks are evolving at a steady pace. Every step we make in this direction is providing us with a much better understanding of both the basics of our brain and mental functions, and the way machines could be able to “think” independently.
The relationship between neurotechnologies and artificial neural networks (ANN) is becoming progressively more bi-directional.
Every day, ANNs become more advanced as we find out more about our brains, and that neurotechnologies benefit from the deeper understanding of the basic processes and communication channels between neurons that are exemplified by ANNs. (Read 6 Big Advances You Can Attribute to Artificial Neural Networks.)
Singularity is, hands down, one of the most fascinating evolutions of artificial intelligence (AI). The idea that a machine created by human hands may, at the very least, could eventually possess the mental abilities of a small child has always been core material for countless fictional ideas.
But, even the simplest biologic system is far more complex than the most advanced AI that we devised today.
What is Neurotechnology?
Neurotechnology is a really broad term that encompasses any technology that could help us obtain a better understanding of the brain. It may be argued that ANNs actually are a neurotechnology since they help us understand the complexity of biologic neural networks.
At the same time, many neurotechnologies constitute the basis upon which the most modern ANNs have been modeled as the more we know about human brains, the more intelligent programmed machines will be.
The definition of neurotechnology embraces any instrument or technology that provides us an explanation on what constitutes the human brain from a physiological and biochemical point of view, as well as consciousness, thought processes, and thinking patterns.
In this sense, an MRI scan of the brain that provides us with a detailed imaging report of the processes underlying neural functions is a neurotechnology.
However, even psychoactive drugs and medications that act as chemical modulators in the central nervous system (such as antidepressants) constitute neurotechnologies in the deepest sense of the word.
Some traditional neurotechnologies, such as brain imaging, have been recently reinvented into innovative devices aimed at improving our understanding of higher brain functions.
For example, electroencephalographic (EEG) headbands have been experimented as wearable neuroimaging devices that should track students’ brain activities as they perform various tasks.
The idea, in this case, is to be able to associate physiological and electrochemical brain activity to more advanced behavioral functions such as thinking, studying, or changing mindset to adapt to complex intellectual challenges.
What Are Neural Networks?
Neural networks are a set of algorithms that try to mimic the ability of biologic brains to interpret sensory data by translating it into data that can be recognized by a machine. (Download A Laymen’s Guide to Neural Networks.)
Neural network aggregate, parse, and label raw input to transform sensory data such as images, text or sound into machine data. They need to translate all real-world information into numerical vectors, and then identify the patterns required to classify and make sense of them.
They help us understand how higher beings such as humans and animals correlate info and transform inputs into outputs.
There’s a huge difference between a biological neural network and an ANN though. Although we have been inspired by birds and horses to build airplanes and trains, neither one of these technological marvel may even get close to the awesome complexities of animal musculoskeletal systems.
Machines are able to “weight” different signals to assign them different values and produce more articulated outputs. Living neurons are actually able to talk to each other, modulate their signals, create and destroy connections between their axons and dendrites and much more.
All this occurs asynchronously as each node is able to act independently within the network — something that artificial layers still can’t do since they must compute one by one.
Size is also on a completely different level, with human brains containing over 86 billion neurons compared to the few hundreds (maybe thousands) of modern neural networks.
The Latest Technological Developments
Brain-computer interfaces (BCI) implanted in human brain tissue are able to interact with neurons by stimulating on inhibiting them. They are used for medical purposes ranging smart prosthetics that can be controlled by concentrating on them (neuroprosthetics) to noninvasive headbands that contrast neuropsychiatric conditions by modifying emotions and cognition.
Ambitious international projects such as the Brain Initiative have begun to establish a direct connection between brain functions and computational models.
Artificial diagrams that describe every neural circuit of the human brain at the neuromolecular level are being developed to collect and analyze brain data.
The idea is to uncover the incredibly articulated components that link brain function and behavior, and allow human minds to process info at the speed of thought (pun intended).
Other pie-in-the-sky projects, such as Elon Musk’s famous Neuralink, instead, hope to achieve much more futuristic goals such as giving humans the ability to project their thoughts to other humans.
Many argue whether those are just some Black Mirror-esque dystopian illusion rather than a possible future reality, however.
What We’ve Learned
The more we augment neural networks, the more we learn how to use our brains to their fullest potential.
Albeit in a very abstract way, we may argue that this is the first step in linking human minds to computers. In other words, we just devised the least invasive form of brain-machine interface we could possibly think of.