Reliable storage and recall of aperiodic spatiotemporal activity patterns using scaffolded attractors

Afzal, Muhammad Furqan; Minai, Ali A.

doi:10.1109/ijcnn.2016.7727451

2016 International Joint Conference on Neural Networks (IJCNN) 2016

DOI: 10.1109/ijcnn.2016.7727451

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Reliable storage and recall of aperiodic spatiotemporal activity patterns using scaffolded attractors

Muhammad Furqan Afzal

Ali A. Minai

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2018

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Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks

Katz

Reggia

2018

IEEE Trans. Neural Netw. Learning Syst.

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We introduce mathematical objects that we call "directional fibers," and show how they enable a new strategy for systematically locating fixed points in recurrent neural networks. We analyze this approach mathematically and use computer experiments to show that it consistently locates many fixed points in many networks with arbitrary sizes and unconstrained connection weights. Comparison with a traditional method shows that our strategy is competitive and complementary, often finding larger and distinct sets of fixed points. We provide theoretical groundwork for further analysis and suggest next steps for developing the method into a more powerful solver.

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Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks

Katz

Reggia

2018

IEEE Trans. Neural Netw. Learning Syst.

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show abstract

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Reliable storage and recall of aperiodic spatiotemporal activity patterns using scaffolded attractors

Cited by 1 publication

References 51 publications

Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks

Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks

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