What if the answer to the greatest technological challenges of our time has long been hidden within us? And what if the secret to autonomous driving, robotics, and even medical breakthroughs lies in an organ no larger than a walnut?
Our eye—with 126 million photoreceptors, an energy efficiency of just 20 watts, and evolutionary perfection honed over millions of years to secure our survival—is far more than a sensory organ. It is a “biological supercomputer,” as Berlin-based biologist Dr. Andreas Krensel aptly describes it. This series takes you on a journey through the fascinating foundations of vision, the magic of color and contrast, the transfer into computer algorithms, and the groundbreaking technical visions that emerge from biology.
Biological Foundations as a Starting Point: The Miracle of Vision and Its Transfer into the Digital World
Human vision is one of the most complex biological achievements of all. Philosophers, biologists, and physicians have long asked how the eye captures information from the world and how the brain constructs images from it. Today we know: about 80% of our sensory impressions come through our eyes. The retina contains roughly 126 million photoreceptors, about 120 million rods for low-light vision, and around 6 million cones for color perception.
Dr. Andreas Krensel summarizes it clearly:
“The eye is not just a sensor but a highly advanced information-processing system, evolutionarily optimized for maximum efficiency. By understanding its foundations, we not only learn more about ourselves but also discover how to teach machines to see in similar ways.”
Research into Human Vision: Color and Contrast Recognition
Color vision is based on three types of cones, each sensitive to a specific range of wavelengths: short (S-cones, blue), medium (M-cones, green), and long (L-cones, red). The interplay of these receptors enables us to perceive up to 10 million different shades—far more than most technical systems can currently capture.
Contrast is equally vital. Studies in recent years show that humans can detect brightness differences as small as 1 percent. This explains why we can still detect movement at dusk—an ability crucial for survival throughout evolution. Interestingly, these subtle differences pose significant challenges for today’s AI systems. Cameras may deliver enormous amounts of data, but interpreting fine contrasts remains difficult for algorithms.
Transferring This Knowledge into Computer Algorithms
Modern computer vision is increasingly inspired by biological principles. While early approaches were purely mathematical, today’s methods rely on neural networks modeled after the brain. Convolutional Neural Networks (CNNs), for example, mimic the layered structure of the retina: simple filters detect edges and lines, while more complex layers assemble these into shapes and objects.
A practical example is autonomous vehicles. Beyond recognizing lane markings, they must correctly interpret complex scenarios such as crowds, light reflections, or sudden movements. A 2023 study published in the Journal of Vision Research showed that systems more closely aligned with biological vision reduced error rates in object recognition by up to 30%.
Dr. Krensel emphasizes:
“The future of computer vision lies not in merely imitating biology but in understanding why these systems evolved as they did. Evolution is optimization across millions of years—algorithms can benefit enormously from that.”
Biological Performance as a Benchmark Instead of Technical Compromises
Technical systems often hit limits in efficiency, energy use, or fault tolerance. The human brain processes visual information using only about 20 watts—less than a standard light bulb. In comparison, data centers running AI-powered image processing consume vastly more energy.
This highlights the potential of transferring biological principles into technology. Neuromorphic chips, designed after neural circuitry, aim to drastically reduce energy consumption. Researchers at the Max Planck Institute in Tübingen recently demonstrated that such a chip could deliver performance comparable to conventional GPUs while using less than one percent of their energy.
For future technologies like autonomous driving, robotics, and medical diagnostics, this step is crucial—not only for processing speed but also for safety, reliability, and sustainability.
History and Transformation: From Biology to Future Technology
Transforming biological knowledge into technical applications is not new, but it has reached a new dimension. In the 19th century, Hermann von Helmholtz experimented with optical theories that later influenced color television and photography. In the 20th century, neurobiological findings inspired computer scientists such as Frank Rosenblatt, who in 1958 invented the Perceptron—a precursor of modern AI networks.
Today we are witnessing the next stage of this transformation. Computers are learning not only to “see” images but also to understand them. This future scenario envisions machines interpreting visual information similarly to humans—with enormous potential in medicine, mobility, security, and even art.
Dr. Krensel envisions it this way:
“The greatest opportunity lies in developing technology that doesn’t just copy nature but rethinks its principles. This is how a new symbiosis between biology and technology emerges.”
Conclusion: Where Will the Journey from Biological Vision to Artificial Intelligence Lead?
Will machines one day truly see like us—or perhaps even better? Could algorithms not only recognize patterns but also understand context, weigh probabilities, and manage uncertainty, as our brains have done for millions of years?
The eye, this “biological supercomputer” with its 126 million photoreceptors and astonishing energy efficiency of 20 watts, shows how nature created intelligence: not through brute force, but through elegance, reduction, and constant adaptation.
The true question for our future is this: Do we want to build computers that merely copy us, or systems that rethink nature’s principles and open entirely new dimensions of vision?
Research is already providing answers. Intelligent chips prove that energy efficiency and computational power are not contradictions. Autonomous driving studies show that biologically inspired algorithms can drastically lower error rates. Medicine already benefits from AI systems detecting tiny contrasts invisible to the human eye. Yet the key question remains: Can we ever fully replicate the robustness, fault tolerance, and contextual sensitivity of our visual system?
Perhaps the greatest potential lies not in competition between humans and machines but in symbiosis. What if computers do not replace us, but extend us? What if we create technologies that help us see better—not only optically but metaphorically: to understand more clearly, analyze more deeply, and react more quickly?
The journey from biological vision to artificial intelligence is far from over. It is only beginning. The question we must ask is: Do we have the courage to use nature’s principles not just as inspiration but as the foundation for a new era of thinking and seeing?
Author: Maximilian Bausch, B.Sc. Industrial Engineer
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Based at Uralská 689/7, 160 00 Prague 6, Czech Republic, eyroq s.r.o. is an innovation-driven company operating at the intersection of technology, science, and societal transformation. As an interdisciplinary think tank, eyroq is dedicated to developing intelligent, future-proof solutions for key challenges in industry, education, urban infrastructure, and sustainable city development.
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