A Unified Approach to Building and Controlling Spiking Attractor Networks
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Publication:4678373
DOI10.1162/0899766053630332zbMath1087.68614OpenAlexW2150439267WikidataQ51462169 ScholiaQ51462169MaRDI QIDQ4678373
Publication date: 23 May 2005
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/0899766053630332
Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)
Related Items (10)
Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers ⋮ Function approximation in inhibitory networks ⋮ Synthesis of recurrent neural networks for dynamical system simulation ⋮ Computational capabilities of analog and evolving neural networks over infinite input streams ⋮ Population Models of Temporal Differentiation ⋮ Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells ⋮ Design of Continuous Attractor Networks with Monotonic Tuning Using a Symmetry Principle ⋮ Unnamed Item ⋮ Flexible Working Memory Through Selective Gating and Attentional Tagging ⋮ Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks
Cites Work
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- A general framework for neurobiological modeling: an application to the vestibular system
- Vector reconstruction from firing rates
- Titration of chaos with added noise
- Stationary Bumps in Networks of Spiking Neurons
- Information Processing in Dendritic Trees
- AUTONOMOUS EXPLORATION AND CONTROL OF CHAOTIC SYSTEMS
- Neural networks and physical systems with emergent collective computational abilities.
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