Randomly Connected Networks Have Short Temporal Memory

DOI10.1162/NECO_a_00449zbMath1415.92026OpenAlexW2089552695WikidataQ45197548 ScholiaQ45197548MaRDI QIDQ5378199

Peter E. Latham, Hamid Reza Maei, Edward C. Wallace

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00449

zbMATH Keywords

mammalian brain connectivityrandomly connected neural networksshort temporal memory

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Memory and learning in psychology (91E40)

Related Items (4)

Short-Term Memory Capacity in Networks via the Restricted Isometry Property ⋮ Sequence Memory Based on Coherent Spin-Interaction Neural Networks ⋮ A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks ⋮ Rate chaos and memory lifetime in spiking neural networks

Cites Work

• Edge of chaos and prediction of computational performance for neural circuit models
• On the sensitive dependence on initial conditions of the dynamics of networks of spiking neurons
• Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons
• From attractor to chaotic saddle: a tale of transverse instability
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
• Polychronization: Computation with Spikes

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