Learning, memory, and the role of neural network architecture.

ABSTRACT: The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in net

SUBMITTER: Hermundstad AM 

PROVIDER: S-EPMC3127797 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature