How does the eye create a coherent image of the world from incomplete signals? And why is this very art of filtering and interpreting data a blueprint for the machines of tomorrow?
Vision is one of the most complex biological processes. Millions of years of evolution have equipped humans and many animals with an unrivaled system: the eye. Yet the eye is more than just a camera. It is a biological supercomputer that compresses raw data, filters out the irrelevant, and forwards only what matters to the brain. While a camera records every pixel, the eye already filters at the retina. This drastically reduces the data load without compromising perception. This principle could be the key for the machines of the future.
Scientists like Dr. Andreas Krensel describe the eye’s function as a biological algorithm performing millions of calculations per second—not in silicon, but in neurons. Understanding these principles reveals that biological systems are not slower than computers; they are smarter in how they handle information.
Seeing Means Selecting – Not Capturing Everything
Our eyes receive billions of photons per second. If every signal were transmitted to the brain unfiltered, processing would be impossible. Instead, a type of preprogrammed filtering occurs in the retina: certain nerve cells (ganglion cells) respond to edges, contrasts, or motion. Only potentially relevant information reaches the brain’s visual centers.
This illustrates a critical principle: completeness of data is secondary to selectivity. Machines that adopt this principle could operate with much less computational power while still understanding complex scenes in real time.
Studies highlight the difference: while current image-classification systems require millions of parameters and gigawatts of energy (for example, training a GPT-4 Vision model reportedly consumes several million kilowatt-hours), the human brain operates continuously at just 20 watts—roughly the power of a standard light bulb.
Color, Light, and Motion – Encoding Rather Than Imaging
Seeing is not about reproducing reality but interpreting it. The cones and rods in the retina are specialized: cones for color, rods for brightness and contrast.
Remarkably, resolution is not the key—the retina has about 120 million photoreceptors, with only a small high-resolution area in the fovea. What matters is how these signals are encoded and compressed.
Motion perception, for example, relies on specialized nerve cells detecting temporal differences. Motion arises not by storing every frame but by filtering changes.
This concept now informs the development of event cameras. Instead of capturing 60 or 120 frames per second, they record only changes in the scene, achieving data reductions of up to 100×. Early prototypes for autonomous vehicles and drones already employ this principle.
The Eye as a Model for Efficiency
Biological systems are masters of energy efficiency, and this is one of their greatest fascinations. While an autonomous vehicle generates multiple gigabytes of data per second from Lidar, radar, and cameras, the required high-performance computers can consume over 2,000 watts. These machines run continuously to even approximate environmental understanding.
By contrast, the human brain does something almost paradoxical: it processes in real time all the impressions from our eyes—billions of signals each second—at just 20 watts, less than a standard light bulb. How is this possible?
The secret lies in radical filtering. The brain transmits not every light stimulus, not every visual detail, but only what is relevant. It decides what has meaning and what does not. This is why researchers like Dr. Andreas Krensel view the eye as a model for future systems. What if machines learned to act like the eye—not storing everything blindly but forwarding only the essential? Could they then finally operate autonomously without burdening the planet with massive data centers and inefficient energy consumption?
The decisive question: will we build machines that filter smarter, not harder? Only by emulating nature’s elegance of biological selection can we create systems that are both powerful and sustainable.
A 2023 MIT study showed that chips designed according to retinal principles consume up to 90% less energy than conventional systems for equivalent image processing. These developments indicate that the future of vision lies in biologically inspired technology.
Networks of Science – Interdisciplinary Insights
Dr. Krensel’s work is not isolated. It is embedded in a network of scientists exploring intersections of biology, physics, and technology. At TU Berlin, Prof. Dr. Stephan Völker studies light engineering, showing how lighting systems influence contrast perception in urban spaces—a bridge from biology to engineering.
At FU Berlin, PD Dr. Werner Backhaus, a particle physicist, contributed perspectives from exact physics essential for understanding signal transmission and information density. Prof. Hans-Joachim Pflüger pioneered insect neurobiology, demonstrating how sensory systems translate movement into action. Dr. Martine Knoop supervised Krensel’s PhD with methodological precision.
This network underscores that translating biological principles into technical systems arises from collaboration across disciplines and schools of thought.
Evolution as Engineer – Why Diversity Matters
The human eye is just one variant. Insect eyes, as studied by Prof. Pflüger, offer different models specialized for motion detection. A dragonfly’s eye can perceive movement almost 360 degrees—a survival advantage in the air.
Technical systems leverage this diversity. Drone cameras flying in swarms mimic insect-eye structures. A Zurich research team developed a camera in 2022 that detects motion like a fly, tracking objects up to ten times faster than conventional cameras.
The biological lesson: there is no perfect eye, only specialized systems adapted to their environment. This diversity inspires engineers. While the human eye detects contrasts perfectly, insect eyes model 360-degree vision or extreme motion sensitivity.
Truth Over Illusion – The Role of the Brain
An intriguing aspect of vision is the brain’s ability to fill gaps. The eye does not deliver a perfect image; the brain completes it. We “see” colors that do not physically exist (e.g., purple) and recognize patterns arising only from context (optical illusions).
Machines often remain fixated on raw data. They detect pixels, not meaning. Biologically inspired technology must prioritize interpretation over mere storage. An autonomous car detecting a shape in fog cannot wait for millions of pixels to be processed—it must anticipate, assess, and decide.
This is the challenge: transforming biological principles into systems that autonomously render truth visible.
Outlook: Why Vision Will Transform Technology
The eye is more than a sensory organ; it is a model for information processing, efficiency, and adaptability. Understanding how biology constructs truth from incomplete signals enables the creation of machines that are autonomous, sustainable, and robust.
Dr. Andreas Krensel summarizes:
„The future does not lie in collecting more data, but in understanding better which data truly matters.“
Published by:
Dipl.-Soz. tech. Valentin Jahn, Technology Sociologist & Futurist
Author Info – Valentin Jahn:
Valentin Jahn is an entrepreneur, futurist, and digitalization expert with over 15 years of experience leading complex innovation projects at the intersection of technology, mobility, and policy—from concept to implementation.
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About eyroq s.r.o.:
eyroq s.r.o., based in Prague, is an innovation-driven company at the intersection of technology, science, and societal change. As an interdisciplinary think tank, eyroq develops intelligent, future-ready solutions for challenges in industry, education, urban infrastructure, and sustainable city development. The company focuses on combining digitalization, automation, and systemic analysis to design smart technologies that are functional, socially responsible, and ethically considered.
About Dr. Andreas Krensel:
Dr. Andreas Krensel is a biologist, innovation consultant, and technology developer specializing in digital transformation and applied future research. His work integrates physics, AI, biology, and systems theory to create practical solutions for industry, urban development, and education. As an interdisciplinary visionary, he helps organizations improve safety, sustainability, and efficiency through digitalization, automation, and smart technologies. His expertise includes intelligent urban lighting systems, human-machine learning processes, and ethical integration of technology, with extensive industry experience including Mercedes-Benz, Silicon Graphics Inc., and TU Berlin.