Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells

DOI10.1162/NECO_A_01046zbMath1472.92045OpenAlexW2773393927WikidataQ47268872 ScholiaQ47268872MaRDI QIDQ5157139

Aaron R. Voelker, Chris Eliasmith

Publication date: 12 October 2021

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

Full work available at URL: https://doi.org/10.1162/neco_a_01046

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (3)

Generation of scale-invariant sequential activity in linear recurrent networks ⋮ Computing with Spikes: The Advantage of Fine-Grained Timing ⋮ Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks

Uses Software

• Seaborn
• Nengo
• SpiNNaker

Cites Work

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• Developing and applying a toolkit from a general neurocomputational framework
• On the Lambert \(w\) function
• A Scale-Invariant Internal Representation of Time
• Computation of system balancing transformations and other applications of simultaneous diagonalization algorithms
• On computing the worst-case norm of linear systems subject to inputs with magnitude bound and rate limit
• Population Models of Temporal Differentiation
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Practical Extrapolation Methods
• A Unified Approach to Building and Controlling Spiking Attractor Networks

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