PROPOSAL
This research project examines the convergence of biological and computational vision systems, investigating how neural networks process visual information in ways that both mirror and diverge from human perception.
Research Questions: How do convolutional neural networks replicate the hierarchical processing of the visual cortex? What insights can neuroscience provide for improving artificial vision systems? How might understanding computational vision reshape our understanding of human perception?
Methodology: The project employs a mixed-methods approach combining computational experiments, comparative analysis of biological and artificial neural networks, and theoretical investigation of perception and representation. Using Swift, Metal, and Core ML, we develop custom vision systems that allow for granular examination of feature extraction and pattern recognition.
Expected Outcomes: This research aims to contribute new frameworks for understanding the relationship between human and machine vision, with implications for both AI development and cognitive science. The work will result in a series of experimental prototypes, research papers, and visual documentation.
PROJECTS
VISUAL_FIELD_STUDY
Interactive system examining how convolutional layers extract hierarchical features from visual input, mapping the transformation from pixel data to semantic understanding.
PARALLEL_PROCESSING
Real-time comparison of biological and computational parallel processing strategies, visualizing how both systems distribute and integrate information across multiple channels.
ATTENTION_MAPPING
Exploring attention mechanisms in both human vision and transformer architectures, revealing the computational similarities in how systems prioritize and weight visual information.
ABOUT
This research emerges from a background spanning neuroscience, computer science, and visual art. The intersection of these disciplines provides a unique perspective on how biological and artificial systems process visual information.
Working primarily with Swift, Metal, and Core ML, the research develops custom tools and frameworks that allow for detailed examination of visual processing at multiple scales—from low-level feature extraction to high-level semantic understanding.
The work has been presented at various conferences and exhibitions, contributing to ongoing dialogues about the nature of perception, the future of artificial intelligence, and the philosophical implications of machine vision.
For inquiries: research@example.com