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Architectural constraints on learning and memory function

✍ Scribed by Ann M Hermundstad; Kevin S Brown; Danielle S Bassett; Jean M Carlson

Publisher
BioMed Central
Year
2011
Tongue
English
Weight
137 KB
Volume
12
Category
Article
ISSN
1471-2202

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✦ Synopsis

Recent experimental and computational studies have identified relationships between architecture and functional performance in information processing systems ranging from natural neuronal ensembles [1,2] to artificial neural networks [3,4]. While these systems can vary greatly in their size and complexity, they share certain structural features, such as parallel and layered motifs [5]. Quantifying how these features influence functionality is a first step toward understanding the behavior of both natural and artificial information processing systems. Of particular interest is the impact of structural architecture on the ability of the system to balance stability with flexibility, for example in memory versus learning.

In this study, we use neural networks as model information processing systems to examine tradeoffs in learning and memory processes arising from variations in structural organization. We compare the performance

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