Artificial Visual Cortex and Random Search for Object Categorization
| Autor: | Daniel E. Hernández, Eddie Clemente, Aaron Barrera, Sambit Bakshi, Gustavo Olague, Mariana Chan-Ley |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Nervous system
General Computer Science Computer science Genetic programming 02 engineering and technology Random search 0202 electrical engineering electronic engineering information engineering medicine Automatic programming General Materials Science brain-inspired computing Computational model business.industry General Engineering Cognitive neuroscience of visual object recognition 020207 software engineering brain modeling Human brain artificial visual cortex heuristic computing Object (computer science) deep genetic programming Visual cortex medicine.anatomical_structure Categorization Function composition (computer science) 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 54054-54072 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Brain modeling is a research area within computer science devoted to the study of complex and dynamic computing algorithms that imitate brain function regarding the information processing properties of the structures that make up the nervous system. The computational and mathematical structures are composed of interacting modules, whose coordination aims to enhance their problem-solving capabilities. The computational models of the visual cortex use non-trivial interactions between a large number of components. In this paper, we propose a hierarchical structure that mimics the information flow and transformations that take place in the human brain. This paper describes a virtual system composed of an artificial dorsal pathway-or “where” stream-and an artificial ventral pathway-or “what” stream-both are fused to recreate an artificial visual cortex. In previous work, the model was refined through genetic programming to enhance its performance over challenging object recognition tasks. The system finds good solutions during the initial stage of the genetic and evolutionary search. In this paper, the goal is to show that a random search can discover numerous heterogeneous functions that are applied to a hierarchical structure of our virtual brain. Thus, the proposal presents two key ideas: (1) the concept of function composition in combination with a hierarchical structure leads to outstanding object recognition programs, and; (2) multiple random runs of the search process can discover optimal functions. The experimental results provide evidence that high recognition rates could be achieved in well-known object categorization problems; consequently, this paper corroborates the importance of the hierarchical computational structure described in the neuroscience literature. |
| Databáze: | OpenAIRE |
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