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How the Human Visual System Compares to Convolutional Neural Networks

Writer's picture: Alex WadeAlex Wade

The human visual system is an extraordinary feat of biological engineering, capable of recognising faces, detecting movement, and interpreting complex scenes in an instant. In the field of artificial intelligence, convolutional neural networks (CNNs) have been developed to mimic some of these capabilities, powering modern image recognition and processing technologies. While CNNs take inspiration from human vision, they also have fundamental differences. In this blog post, we’ll explore the similarities and differences between these two remarkable systems.


Similarities Between the Human Visual System and CNNs

Layered Processing


Both the human visual system and CNNs rely on hierarchical processing. In the human brain, visual information is processed through multiple stages, starting from the retina and moving through the lateral geniculate nucleus (LGN) before reaching the primary visual cortex (V1). From there, information is passed to higher cortical areas for more complex analysis, such as object recognition and spatial awareness.

CNNs follow a similar principle, using multiple layers to progressively extract features from an image. Early convolutional layers detect simple patterns like edges and textures, while deeper layers identify more complex features such as shapes, objects, and eventually entire scenes.


Feature Detection

Both systems use feature detection to identify important patterns in visual data. The human visual cortex contains neurons that respond to specific visual stimuli, such as horizontal lines, vertical lines, and motion. Similarly, CNNs use convolutional filters to detect edges, colours, and textures at different levels of abstraction.


Parallel Processing

The human visual system processes vast amounts of information in parallel, allowing us to perceive colour, depth, movement, and detail simultaneously. CNNs also use parallel processing to extract features at different levels, making them efficient at handling complex visual tasks.


Key Differences Between the Human Visual System and CNNs

Learning and Adaptability


The human brain learns from a combination of experience, sensory input, and reinforcement. Unlike CNNs, which require massive datasets and backpropagation-based training, humans can learn from a single exposure to an object or concept. Our visual system is also highly adaptable, capable of recognising objects under different lighting conditions, angles, and even when partially obscured.


Energy Efficiency

One of the most striking differences is energy consumption. The human brain operates at around 20 watts—roughly the power consumption of a dim lightbulb—while training a CNN on complex tasks requires vast computational resources, sometimes consuming kilowatts of energy over extended periods.


Robustness and Generalisation

The human visual system is incredibly robust. We can recognise faces in different lighting conditions, identify objects from incomplete information, and generalise knowledge across contexts. CNNs, on the other hand, can be brittle, struggling with variations they were not explicitly trained on. Adversarial attacks, where small perturbations in an image can mislead a CNN, highlight this vulnerability.


Biological Constraints vs Computational Constraints

Human vision is constrained by biological factors such as neuron response times, eye movement limitations, and cognitive biases. CNNs, however, are constrained by computational power, dataset quality, and algorithm design. While the human brain operates continuously and adapts effortlessly, CNNs require structured training phases and may struggle with real-time adaptation without further fine-tuning.


Conclusion

While convolutional neural networks take inspiration from the structure and function of the human visual system, they remain fundamentally different in many ways. The human brain is more energy-efficient, adaptable, and robust in real-world scenarios, while CNNs excel at processing large-scale datasets quickly and with high precision. Understanding these similarities and differences can help improve AI systems by making them more biologically inspired, potentially leading to more efficient and flexible vision-based technologies in the future.

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