Imagine a world where the mysteries of the human brain—our thoughts, emotions, and abilities—could be unlocked faster through collaboration between dreamers and builders. That's the exciting yet divisive challenge at the heart of neuroscience today: it desperately needs engineers, and not just for their gadgets, but for their way of thinking that could revolutionize how we understand the mind. Stick around, because this isn't your typical plea for teamwork—it's a call to rethink the very foundations of scientific discovery.
Before I stepped into my current role in an engineering department, my colleagues in neuroscience cautioned me with a skeptical eye: engineers 'don't grasp biology,' and by extension, they'd never truly connect with someone like me. And there's a grain of truth to that stereotype. In my classrooms, most students aren't captivated by the intricate anatomy, inner workings, or evolutionary history of the nervous system. Instead, they're fired up about crafting robots and AI language systems that sidestep biology's convoluted approaches to everyday challenges, like mastering the art of walking or speaking.
This divide between 'studying and deciphering' versus 'inventing and constructing' is not only tolerated but actively fostered in academic circles and broader society. Take my own department at the University of Cambridge, for instance—it's grouped under the School of Technology, alongside computer science and management studies. Meanwhile, many of my neuroscience peers reside in the School of Biological Sciences. I argue that this artificial separation is actually harming the field. We urgently require more engineers—and, crucially, more of their innovative problem-solving perspective—in neuroscience.
Now, I won't waste time repeating the straightforward benefits: engineers create the instruments and devices that let us probe the brain with unprecedented precision. But let's dive deeper. To propel and refine scientific advancement in neuroscience, we should emulate the questions engineers pose and incorporate their methodologies, thought processes, and philosophies. An engineering approach—focusing on logical fixes for clear objectives while navigating real-world limitations—could steer neuroscience research effectively. Why? Because brains, and life sciences as a whole, exemplify purposeful mechanisms in an otherwise chaotic cosmos. These aren't random; underlying principles govern their functionality.
To illustrate this, I'll briefly venture beyond pure neuroscience (though you could generously argue it's still within neuroethology, the study of animal behavior and brain functions). It's a popular joke that we'd never have invented functional airplanes if we'd merely copied birds directly. Effective plane designs ditched flapping wings for alternative propulsion methods. Yet, experts widely agree that key early inspirations for aviation sprang from studying and dissecting bird flight (as detailed in a fascinating paper at https://doi.org/10.1242/jeb.245409). For beginners, think of it like this: birds inspire us, but engineers refine those ideas into something practical.
Fundamental wing shapes—and especially how wings and tails morph into 'control surfaces' that produce lift, guide, and stabilize, as aerospace pros call them—are more essential to powered flight than propulsion alone. Without these basics, airplanes couldn't soar. Propulsion, on the flip side, has powered rockets since ancient China's dynasties. This shows how aviation blends biological insights with engineered abstractions in a seamless exchange of concepts—much like the intertwined histories of connectionist AI and neurophysiology, or computer science and cognitive science. Pioneers like Frank Rosenblatt, Warren McCullough, and Walter Pitts were neurophysiologists and engineers who modeled the brain as the ultimate computing archetype in the early days of cybernetics. Simultaneously, Alan Turing, often hailed as computing's father, was motivated by his quest to unravel human thinking.
Over the years, debates raged between advocates of 'brain-mimicking' connectionist AI and those who dismissed it as a detour from pure statistical theories. But here's where it gets controversial: I firmly believe brain-inspired AI is what powers the tech we rely on today. Some might resist this view, craving to untangle applied inventions from their theoretical roots, especially when those ideas evolve far beyond their biological origins. Humans also tend to oversimplify, categorizing ideas and even people into neat boxes. Even after airplanes took off, legendary aerospace engineer Theodore von Kármán, who earned the 1962 National Medal of Science for Engineering, famously quipped (via https://transmitter.ieee.org/10-quotes-spark-engineering-inspiration/) that 'Scientists study the world as it is; engineers create the world that has never been.' But is this creation truly from scratch, or is it molded by what we already know about nature? Note the emphasis on individuals—'scientists' versus 'Scientists,' 'engineers' versus 'Engineers'—highlighting our love for dividing people, careers, departments, and even thoughts into binary camps.
And this is the part most people miss: a review of bird flight research (at https://doi.org/10.1139/cjz-2015-0103) notes that animal flight experts now use multilevel systems methods borrowed from engineering, like control and dynamical systems analysis. There's a poetic beauty in ideas from a century-old, pragmatic field circling back to illuminate the natural wonders that sparked them. Our hands-on building efforts ultimately deepen our grasp of biology.
I contend this cycle is unfolding—and should be sped up—across neuroscience. Major breakthroughs in brain operations often stem from attempts to replicate them. This insight could reshape research agendas, without abandoning pure exploration; after all, innate curiosity fueled aviation's start. An engineering lens might refine ambitious neuroscience projects by sharpening objectives.
For instance, simulations mimicking neural data are standard, aiming to uncover brain secrets. But why not prioritize models for real-time forecasts (as explored in https://doi.org/10.1038/s41551-020-00666-w) to allow precise brain state tweaks?
Consider the NIH's BRAIN Initiative, which initially aimed to monitor as many neurons as feasible (detailed in https://doi.org/10.1016/j.cell.2017.10.026), assuming more data equals better insights—and it sparked immediate controversy (as reported in https://www.science.org/content/article/white-house-embraces-brain-initiative-questions-linger). Reframing goals around engineering targets clarifies things: What neural detail suffices for accurately interpreting actions or operating brain-machine links instantly? Superior data and tech would naturally emerge.
Success or failure here would profoundly impact theories of brain information coding. Clear benchmarks elevate the importance, building confidence in discovered principles. Returning to flight: Knowing Bernoulli's principle (which links fluid speed to pressure, like how wings create lift) is one thing; soaring safely at 40,000 feet is another.
This approach is already gaining traction: Brain-computer interfaces (covered in https://doi.org/10.1016/j.neuron.2015.03.036) serve as research aids and potential treatments. Predictive models enable precise actions in living brains, such as forecasting and halting seizures (see https://doi.org/10.1038/s41582-018-0055-2).
You might be wondering at this point: 'What about physicists?' Indeed, physicists have advanced brain knowledge (though they've led us astray at times!). When they aim to alter brain functions or build brain-like systems, they're essentially engineering, training aside.
Yet, engineers possess unique expertise over other 'quantitative' scientists, including physicists. They design dependable systems—not vaguely, but concretely, ensuring complex interactions that support lives. They master feedback loops, the 'reality gap' between plans and outcomes, and a humble acceptance of unknowns. You'd never see an engineer chasing a 'grand unified brain theory' (spoiler: there isn't one!).
In essence, engineers excel at making things functional, blending core principles with practical realities. This aligns engineering more with biology than other sciences—biology thrives on purpose, not mere chance.
So, let's welcome engineers into neuroscience as full partners. And perhaps we all should adopt some engineering thinking occasionally. What do you think? Does merging these fields risk oversimplifying biology's complexity, or is it the key to unlocking the brain's secrets? Share your views in the comments—do you agree we need more engineers in neuroscience, or fear it might dilute pure scientific curiosity?
