Mahowald, Misha (1992) VLSI analogs of neuronal visual processing: a synthesis of form and function. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:09122011-094355148
This thesis describes the development and testing of a simple visual system fabricated using complementary metal-oxide-semiconductor (CMOS) very large scale integration (VLSI) technology. This visual system is composed of three subsystems. A silicon retina, fabricated on a single chip, transduces light and performs signal processing in a manner similar to a simple vertebrate retina. A stereocorrespondence chip uses bilateral retinal input to estimate the location of objects in depth. A silicon optic nerve allows communication between chips by a method that preserves the idiom of action potential transmission in the nervous system. Each of these subsystems illuminates various aspects of the relationship between VLSI analogs and their neurobiological counterparts. The overall synthetic visual system demonstrates that analog VLSI can capture a significant portion of the function of neural structures at a systems level, and concomitantly, that incorporating neural architectures leads to new engineering approaches to computation in VLSI. The relationship between neural systems and VLSI is rooted in the shared limitations imposed by computing in similar physical media. The systems discussed in this text support the belief that the physical limitations imposed by the computational medium significantly affect the evolving algorithm. Since circuits are essentially physical structures, I advocate the use of analog VLSI as powerful medium of abstraction, suitable for understanding and expressing the function of real neural systems. The working chip elevates the circuit description to a kind of synthetic formalism. The behaving physical circuit provides a formal test of theories of function that can be expressed in the language of circuits.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Subject Keywords:||Computation and Neural Systems|
|Degree Grantor:||California Institute of Technology|
|Major Option:||Computation and Neural Systems|
|Thesis Availability:||Restricted to Caltech community only|
|Defense Date:||12 May 1992|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Dan Anguka|
|Deposited On:||12 Sep 2011 17:06|
|Last Modified:||26 Dec 2012 04:38|
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