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Catherine D. Schuman

Researcher at Oak Ridge National Laboratory

Publications -  132
Citations -  2723

Catherine D. Schuman is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Neuromorphic engineering & Artificial neural network. The author has an hindex of 20, co-authored 112 publications receiving 1791 citations. Previous affiliations of Catherine D. Schuman include University of Tennessee.

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A Survey of Neuromorphic Computing and Neural Networks in Hardware.

TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
Proceedings Article

A performance evaluation and examination of open-source erasure coding libraries for storage

TL;DR: A head-to-head comparison of codes and implementations is performed, to discern whether theory matches practice, and to demonstrate how parameter selection, especially as it concerns memory, has a significant impact on a code's performance.
Journal ArticleDOI

Opportunities for neuromorphic computing algorithms and applications

TL;DR: A review of recent results in neuromorphic computing algorithms and applications can be found in this article , where the authors highlight characteristics of neuromorphic Computing technologies that make them attractive for the future of computing and discuss opportunities for future development of algorithms and application on these systems.
Journal ArticleDOI

Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.

TL;DR: It is demonstrated that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing.
Proceedings ArticleDOI

An evolutionary optimization framework for neural networks and neuromorphic architectures

TL;DR: This work describes an EO training framework for a spiking neural network architecture and a neuromorphic architecture, and presents the results of this training framework on four classification data sets and compares those results to other neural network and neuromorphic implementations.