Technological advancements in the last few years are rapidly beginning to mirror what we once thought was the realm of science-fiction. But what is driving this innovation? Some would argue that it’s our understanding of the human brain, that we are on the verge of reverse engineering
its processes and mechanisms to create artificial algorithms and machines that are capable of
extraordinary things like restoring our senses. Blog inspired by The Cellular Republic's video.
In 2020, DeepMind solved a 50-year-old biology problem of predicting the structure of protein
molecules in human DNA using a brain inspired artificial intelligence (AI) algorithm. It has the
potential to tackle diseases and discover new medicine, potentially accelerating every field of
biology (or life itself). Computational neuroscience is the driving force behind these advances. The human brain has around 80 billion neurons. The activities we perform in our daily lives directly result from these neurons creating various hypercomplex connections in the brain. Computational neuroscience attempts to decode this brain mystery.
We perceive information and learn from our experiences. Without perception, it is not possible
to understand the world around us. If we can learn how our brain functions whenever we
perform a task, we can mimic this behavior using biologically-inspired machines to advance our
cognitive abilities further. This forms the basis of computational neuroscience. In this article, we’ll discuss computational neuroscience and its various applications. We’ll also expand on its frightening potential! In the end, we’ll discuss where computational neuroscience is headed in the near future.
What is Computational Neuroscience?
Computational neuroscience is the study of brain activity that uses mathematical models to identify highly complex structural connectivity between neurons that process brain information. It is an interdisciplinary field that includes theoretical and practical approaches from biology, physics, computer science, engineering, and statistics to understand brain function at a cellular and molecular level. Sounds quite simple–right? Let’s simplify it more.
Consider the brain as a black box. A regular human has little-to-no idea of what the brains innerworkings and happenings are like. However, a computational neuroscientist seeks to understand about each brain component and its functions. For instance, a psychologist (a very well-educated regular human) would study human moods, emotions, and attitudes to indicate various mental, physical, and social human behaviors. A computational neuroscientist take things another step further.
Computational neuroscientists study which part of the brain was activated in response to a specific behavior. They identify neuronal connections and activation patterns. They also investigate various inputs and environmental variables that resulted in those connections to uncover the unknown factors which caused that behavior. Let’s check out some ultra-futuristic applications that computational neuroscience has to offer--
Mind-Boggling (Pun Intended) Computational Neuroscience Applications
Computational neuroscience is still in its infancy, but we are already seeing some major developments–mostly experimental. Let’s discuss some of these world-changing advances!
Mind Reading (Brain Decoding)
When it comes to brain-inspired technologies, we have to start with mind-reading. Can machines predict what you’re thinking? For a machine to read minds, it first needs to learn a lot about how activity in the brain unfolds and about how this activity might be tied to real things in the outside world. Using new technologies like MRI, we can now feed tons of brain activity into state-of-the-art algorithms to tell the computer what people are looking at when this activity is occurring. With modern AI techniques, we have the building blocks for solving more and more complex mind-reading tasks, for example:
Categorization (Surface Level Mind Reading): Broadly determine the category of what a person is looking at or what objects are present in a scene like a house or an animal.
Identification (Getting Closer to Mind Reading...): Identify what type of object is present in a scene, like a white-colored cat or golden retriever dog. The goal of such tasks is to identify a specific type within a category.
Reconstruction (Mind Reading at its Finest!): Putting all identified objects together to recreate a scene or a memory using computer programs.
With these AI-enabled algorithms and techniques being currently available, computational neuroscientists are already accomplishing all three of these tasks to a certain extent!
However, recording brain activity requires multi-million dollar MRI scanners which are not accessible to everyone. Hence, these experiments are conducted in controlled lab environments backed by a lot of funding. Once we have the basic tools of realizing cognition using machines though, it is possible to understand brain activity and reconstruct it to replay dreams, memories, and thoughts.
If we can understand the brain down to its molecular level, we can demystify neurological disorders. Computational neuroscience can theoretically enable people with paraplegia to walk again. Using brain stimulation, we can give such people the perception of motor and sensory functions. Computational neuroscience can help restoration of senses or support with sensory function--providing the perception of hearing or sight through brain stimulation!
The closest we have come to restoring physiological function is controlling robotic arms using the mind. The DARPA arm is one of the most notable examples. We are still quite a few years away from actually controlling complete brain function but computational neuroscience presents promising directions for humanity.
The Dark Side of Computational Neuroscience
Like every emerging field that shows the slightest possibility of changing the world, conspiracy theories follow. Like robots taking over the world or AI taking over jobs and eventually humanity. Not to mention that billionaires like Elon Musk and Mark Zukerberg who are pushing for brain-computer interface technology–still experimental but adding a lot of weight to such theories. However, most of these notions are based on gossip, myths, and rumors. So, what would happen when the field of computational neuroscience evolves to a degree where it becomes possible to control minds, dreams, or thoughts? Let's explore.
Sure, theoretically, that might be possible to a certain degree but it all sounds more like Charles Xavier from X-Men. To achieve such godly attributes with machines requires gargantuan amounts of computational power, storage, speed, and specialized hardware and at this point, there is a lot left to uncover about the brain first. The current methods of “brain hacking” or remote-controlled brains are experimental at best, with most applications based on the treatment of brain disorders and mental illnesses. So from what we can see, there is no immediate threat to humanity–yet.
What’s Next in Computational Neuroscience?
Among academic researchers, Artificial Intelligence (AI) is finding overlapping use cases in computational neuroscience. Machine learning, a sub-domain of AI, can perform large-scale data analysis and computational modeling related to brain research.
In the coming years, computational neuroscience can result in the development of specialized hardware and software solutions using neuromorphic engineering, advanced brain-inspired technology, and very large-scale integration (VLSI) architectures.
Additionally, fields like neuromarketing, neuroethics and computational psychiatry can present real-world applications very soon. If you want some more inspiration, shop below or click here to get your hands on our latest data-inspired merchandise.