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Memory function in neural and artificial networks 
Book chapter

Memory function in neural and artificial networks 

Daniel L Alkon, Thomas P Vogl, Kim T Blackwell and David Tam
Neural Network Models of Conditioning and Action, pp.1-11
L. Erlbaum Associates
1991

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Abstract

The DYSTAL (Dynamically Stable Associative Learning) model is an artificial modifiable neural network based on observed features of biological neural systems in the mollusc Hermissenda and the rabbit hippocampus. In the DYSTAL network, synaptic weight modification depends on (a) convergence of modifiable “collateral” and unmodifiable “flow-through” inputs,(b) temporal pairing of these inputs, and (c) past activity of elements receiving the inputs. Modification is independent of element output. As a consequence, DYSTAL shows (a) linear scaling of computational effort with network size,(b) rapid learning without an external “teacher,” and (c) ability to complete patterns, independently associate different ensembles of inputs, and to serve as a classifier of input patterns. Our memories are of complex patterns of stimuli—not of individual stimuli in isolation of each other. An image of a face is one such pattern, a melodic refrain is another. It is as if the pieces of a pattern are linked to or associated with each other so that awareness of a critical number of pattern elements or pieces activates the links to restore or recall the entire pattern. Attention to a distinctive scar can cause us to recall the appearance of a face encountered in the past. Hearing a few notes in a particular sequence may trigger a memory of a symphonic movement. Mammalian memory formation may ultimately be reducible to link formation between stimulus elements in a pattern. Two lines of evidence from our laboratory provide support for this hypothesis. Our physiologic observations demonstrate remarkable similarities between molecular and biophysical mechanisms for learning

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