Bridging Human and Artificial Visual Systems

By Abdul Ahad NazakatReviewed by Louis CastelSep 9 2025

Biological vision research and artificial intelligence are increasingly connected as scientists better understand how humans process visual information. This knowledge now directly influences machine vision development across various industries. Companies use bio-inspired technologies in autonomous vehicles, medical imaging systems, and industrial applications. The human visual system runs on roughly 20 watts of power, yet it effortlessly handles tasks that still challenge today’s artificial systems.

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Engineers apply biological principles to create artificial visual systems that can outperform humans in certain areas.¹ Bio-inspired approaches tackle specific problems in traditional computer vision: high power consumption, processing delays, and large dataset requirements. These technologies merge biological efficiency with computational accuracy.²

Key Shared Principles

Human and artificial vision systems use similar processing principles developed through biological evolution and engineering design. These shared principles form the basis for bio-inspired technology development.

Edge Detection and Contour Processing functions are a core component in biological and artificial vision systems. Research shows that feedforward convolutional neural networks can replicate human contour integration behaviors through hierarchical receptive field growth that supports edge linking in artificial networks.³ This principle appears in computer vision applications including object recognition and medical image analysis.

Depth Perception and Spatial Processing in artificial systems uses biological strategies like stereo vision, structured light, and spatio-temporal integration. Current neuromorphic systems combine these methods with event-based sensing for compact depth capture in high-speed applications, replicating how humans quickly assess three-dimensional structure. These systems process spatial information across multiple scales.

Motion Tracking and Temporal Processing use event-based cameras that apply retinal motion detection principles. These cameras capture only visual field changes, providing continuous microsecond-scale timing for motion tracking and optical flow computation with lower data rates than traditional frame-based systems.⁴ This approach supports high-speed visual processing applications with reduced computational requirements.

Visual Attention and Selective Processing mechanisms create sparse event outputs and retina-inspired pre-processing that concentrate computation on temporal changes. This allows segmentation of moving objects and monitoring systems that capture relevant visual changes instead of complete scenes. Attention mechanisms reduce processing requirements while preserving important information.

Foveation and Adaptive Resolution strategies apply the biological approach of high-resolution processing in attention centers while using lower resolution in peripheral areas. Event-based eye trackers sample saccades and micro-movements at kilohertz rates, allowing systems to allocate computational resources based on need.

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Bio-Inspired Visual Technologies

The translation of biological principles into engineering solutions has produced several distinct technology families, each at different stages of development and commercial adoption.

Event-Based Vision Sensors translate retinal processing principles into silicon. These sensors respond to light intensity changes, producing asynchronous event streams rather than traditional frames.⁵ They offer asynchronous pixel operation, high dynamic range exceeding 120dB versus 60dB for conventional cameras, microsecond response times, and reduced data processing requirements.

A company called Prophesee has developed commercial event-based sensors for use in industrial applications, autonomous vehicles, and consumer electronics, showcasing their practical viability in real-world settings.

Neuromorphic computing architectures use synaptic devices to enable brain-like, parallel processing. By integrating memory and computation, much like biological neural networks, they tackle the von Neumann bottleneck, which arises from the traditional separation of memory and processing.⁶ Technologies include spiking neural networks using discrete spikes, memristive devices (electronic components that 'remember' past signals, like biological synapses) with adaptive connections, and in-memory computing that eliminates data movement energy costs.

Intel's Loihi processor demonstrates energy-efficient parallel computation for pattern recognition and learning tasks, showing promise for real-time visual processing where traditional architectures face power and latency constraints.

In-Sensor Neuromorphic Processing combines sensing and neural processing at the pixel level, enabling real-time edge detection directly in the sensor array. This minimizes data movement and enables recognition capabilities.⁷ Recent research demonstrates retina-inspired artificial neurons with broad spectral response for visual preprocessing.

Industrial and Commercial Applications

Bio-inspired vision technologies have moved from laboratory demonstrations to commercial deployment across multiple industries, driven by their unique advantages in power efficiency, speed, and robustness.

Real-time robotics applications benefit significantly from these technologies. In navigation and obstacle avoidance, event-based vision sensors enable robots to navigate complex environments with minimal computational overhead. The sparse, asynchronous data stream inherently segments moving objects and continuously provides depth information, making it well-suited for tasks like obstacle avoidance. For high-speed grasping applications, neuromorphic vision systems support robotic grasping requiring millisecond response times, enabling robots to track and intercept fast-moving objects with precision that exceeds human capabilities.

Optical inspection systems in industrial settings utilize bio-inspired vision for quality control, providing context-aware filtering for manufacturing inspection that focuses computational resources on areas of change or potential defects. This approach significantly reduces false positives and improves inspection throughput. Event-based cameras excel at detecting subtle changes in surface properties or material characteristics that might be missed by traditional frame-based systems, particularly in high-speed production environments.

Biomedical imaging applications demonstrate the clinical potential of these technologies. AI-assisted identification of patterns in diabetic retinopathy using bio-inspired processing shows how artificial systems can identify sex-specific patterns in retinal fundus images, supporting personalized medical diagnosis.⁸ Low-latency neuromorphic vision systems support real-time surgical guidance applications where millisecond response times are critical for patient safety and procedure success.

Key industry players include Prophesee, the leading developer of event-based vision sensors with applications in automotive, industrial, and IoT markets. Intel, through their Loihi neuromorphic processor and research initiatives, advances neuromorphic computing for vision applications, particularly in edge AI and autonomous systems. Research institutions like MIT and ETH Zurich continue to drive fundamental research in bio-inspired vision, with many innovations transitioning to commercial applications through spin-off companies and industry partnerships.

Scientific Research Driving These Technologies

Advances in neuroscience, materials science, and computational methods support bio-inspired vision development. Neuroscience research reveals details about visual cortex hierarchical processing, including predictive coding and feedback loops. These findings translate into artificial systems with top-down processing for improved context understanding.

Materials science enables synaptic devices that mimic biological neural function, supporting adaptive learning in hardware.⁹ New memristive materials (electronic components that "remember" past signals, much like biological synapses) are enabling synaptic devices with multiple states, supporting more complex learning algorithms. By integrating optical and electronic processing, these systems achieve high-bandwidth neural computation that moves closer to the efficiency of the human brain.

Computational neuroscience integration with engineering accelerates practical bio-inspired system development. Advanced spiking neural network algorithms enable neuromorphic system deployment for complex vision tasks.

Challenges and Future Directions

Bio-inspired vision technologies face several key challenges. Current limitations include context understanding, where systems excel at low-level processing but struggle with high-level reasoning that humans perform easily. Many systems show limited ability to adapt to new domains without extensive retraining, and remain vulnerable to adversarial attacks that rarely affect biological systems.¹⁰

Critical breakthroughs are needed in materials technology for scalable synaptic devices, algorithms for translating event streams to semantic understanding, and standardized interfaces between neuromorphic and conventional computing systems. Safety-critical applications require advances in explainability and validation methods.

Future directions in visual computing include hybrid biological-artificial systems, the exploration of quantum-enhanced neuromorphic computing as a potential path forward, and vision systems capable of continuous learning and adaptation throughout their operational lifespan.

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References and Further Reading

1. Kim, M. S., Kim, M. S., Lee, G. J., & Sunwoo, S. H. (2021). Bio-inspired artificial vision and neuromorphic image processing devices. Advanced Materials Technologies, 6(2), 2000851. https://doi.org/10.1002/admt.202000851
   
2. Chen, T., et al. (2025). Orientation Detection in Color Images Using a Bio-Inspired Artificial Visual System. Electronics, 14(2), 239. https://doi.org/10.3390/electronics14020239
   
3. Doshi, F., Konkle, T., & Alvarez, G. A. (2025). A feedforward mechanism for human-like contour integration. PLoS Computational Biology, 21(1), e1013391. https://doi.org/10.1371/journal.pcbi.1013391
   
4. Chen, G., Cao, H., Conradt, J., & Tang, H. (2020). Event-based neuromorphic vision for autonomous driving: A paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Processing Magazine, 37(4), 34-49. https://doi.org/10.1109/MSP.2020.2985815
   
5. Gallego, G., et al. (2020). Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1), 154-180. https://doi.org/10.1109/TPAMI.2020.3008413
   
6. Elfighi, M. M. S. (2025). Advancements and Challenges in Neuromorphic Computing: Bridging Neuroscience and Artificial Intelligence. International Journal For Science Technology And Engineering, 12(1). https://doi.org/10.22214/ijraset.2025.66411
   
7. Ji, Y., et al. (2025). Retina-Inspired Flexible Visual Synaptic Device for Dynamic Image Processing. ACS Applied Materials & Interfaces, 17(1), 123-134. https://doi.org/10.1021/acsami.4c15234
   
8. Delavari, P., et al. (2025). AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images. PLoS One, 20(1), e0327305. https://doi.org/10.1371/journal.pone.0327305
   
9. Zhang, G., et al. (2025). Retina-inspired artificial optoelectronic neurons with broad spectral response for visual image pre-processing. IEEE Electron Device Letters, 46(2), 234-237. https://doi.org/10.1109/LED.2024.3512876
   
10. Zhu, L., Li, J., Wang, X., & Huang, T. (2021). NeuSpike-net: High speed video reconstruction via bio-inspired neuromorphic cameras. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. https://doi.org/10.1109/ICCV48922.2021.01245
    

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