1 July 1992 Physicochemical analog for modeling superimposed and coded memories
Author Affiliations +
Abstract
The mammalian brain is distinguished by a life-time of memories being stored within the same general region of physicochemical space, and having two extraordinary features. First, memories to varying degrees are superimposed, as well as coded. Second, instantaneous recall of past events can often be affected by relatively simple, and seemingly unrelated sensory clues. For the purposes of attempting to mathematically model such complex behavior, and for gaining additional insights, it would be highly advantageous to be able to simulate or mimic similar behavior in a nonbiological entity where some analogical parameters of interest can reasonably be controlled. It has recently been discovered that in nonlinear accumulative metal fatigue memories (related to mechanical deformation) can be superimposed and coded in the crystal lattice, and that memory, that is, the total number of stress cycles can be recalled (determined) by scanning not the surfaces but the `edges' of the objects. The new scanning technique known as electrotopography (ETG) now makes the state space modeling of metallic networks possible. The author provides an overview of the new field and outlines the areas that are of immediate interest to the science of artificial neural networks.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Minas Ensanian, Minas Ensanian, } "Physicochemical analog for modeling superimposed and coded memories", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140081; https://doi.org/10.1117/12.140081
PROCEEDINGS
13 PAGES


SHARE
RELATED CONTENT

Modeling the brain with laser diodes
Proceedings of SPIE (September 09 2007)
Implicit differential analysis for cortical models
Proceedings of SPIE (April 08 2007)
Studies on a network of complex neurons
Proceedings of SPIE (September 01 1993)
Research on land evaluation based on fuzzy neural network
Proceedings of SPIE (February 23 2004)
Temperature compensation for RLG based on neural network
Proceedings of SPIE (December 30 2010)

Back to Top