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Attractors and quasi-attractors of a flow
Journal of Applied Mathematics and Computing, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zuo, Chunyan, Wang, Xiaoxia
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Fractals, 1995
When a chaotic signal is used to force a switching-type variable which dependent on the momentary magnitude of the chaotic stimulus either shows a damped or an autocatalytic response, a new dynamical phenomenon arises. It is reminiscent of the “flares” observed in astrophysical situations.
Rossler, Otto E., Hartmann, Georg C.
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When a chaotic signal is used to force a switching-type variable which dependent on the momentary magnitude of the chaotic stimulus either shows a damped or an autocatalytic response, a new dynamical phenomenon arises. It is reminiscent of the “flares” observed in astrophysical situations.
Rossler, Otto E., Hartmann, Georg C.
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WIREs Cognitive Science, 2009
AbstractAn attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long‐term memory, short‐term memory, attention, and decision making.
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AbstractAn attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long‐term memory, short‐term memory, attention, and decision making.
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Some properties of attractors and quasi-attractors
Chaos, Solitons & Fractals, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Neural Computation, 2001
Attractor networks, which map an input space to a discrete output space, are useful for pattern completion—cleaning up noisy or missing input features. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins.
Zemel, Richard S., Mozer, Michael C.
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Attractor networks, which map an input space to a discrete output space, are useful for pattern completion—cleaning up noisy or missing input features. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins.
Zemel, Richard S., Mozer, Michael C.
openaire +2 more sources