Original language | English (US) |
---|---|
Title of host publication | Encyclopedia of Neuroscience |
Publisher | Elsevier Ltd |
Pages | 667-679 |
Number of pages | 13 |
ISBN (Print) | 9780080450469 |
DOIs | |
State | Published - 2010 |
Abstract
The term attractor, when applied to neural circuits, refers to dynamical states of neural populations that are self-sustained and stable against perturbations. It is part of the vocabulary for describing neurons or neural networks as dynamical systems. This concept helps to quantitatively describe self-organized spatiotemporal neuronal firing patterns in a circuit, during spontaneous activity or underlying brain functions. Moreover, the theory of dynamical systems provides tools for examining the stability and robustness of a neural circuit's behavior, proposes a theory of learning and memory in terms of the formation of multiple attractor states or continuous attractors, and provides insights into how variations in cellular/synaptic properties give rise to a diversity of computational capabilities.
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Keywords
- Associative memory
- Continuous attractor
- Decision making
- Dynamic system
- Molecular switch
- Multistability
- Neuronal spatiotemporal firing patterns
- Persistent activity
- Prefrontal cortex
- Spontaneous up and down states
- Time integration
- Working memory
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Attractor Network Models. / Wang, Xiao-Jing.
Encyclopedia of Neuroscience. Elsevier Ltd, 2010. p. 667-679.Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary
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TY - CHAP
T1 - Attractor Network Models
AU - Wang, Xiao-Jing
PY - 2010
Y1 - 2010
N2 - The term attractor, when applied to neural circuits, refers to dynamical states of neural populations that are self-sustained and stable against perturbations. It is part of the vocabulary for describing neurons or neural networks as dynamical systems. This concept helps to quantitatively describe self-organized spatiotemporal neuronal firing patterns in a circuit, during spontaneous activity or underlying brain functions. Moreover, the theory of dynamical systems provides tools for examining the stability and robustness of a neural circuit's behavior, proposes a theory of learning and memory in terms of the formation of multiple attractor states or continuous attractors, and provides insights into how variations in cellular/synaptic properties give rise to a diversity of computational capabilities.
AB - The term attractor, when applied to neural circuits, refers to dynamical states of neural populations that are self-sustained and stable against perturbations. It is part of the vocabulary for describing neurons or neural networks as dynamical systems. This concept helps to quantitatively describe self-organized spatiotemporal neuronal firing patterns in a circuit, during spontaneous activity or underlying brain functions. Moreover, the theory of dynamical systems provides tools for examining the stability and robustness of a neural circuit's behavior, proposes a theory of learning and memory in terms of the formation of multiple attractor states or continuous attractors, and provides insights into how variations in cellular/synaptic properties give rise to a diversity of computational capabilities.
KW - Associative memory
KW - Continuous attractor
KW - Decision making
KW - Dynamic system
KW - Molecular switch
KW - Multistability
KW - Neuronal spatiotemporal firing patterns
KW - Persistent activity
KW - Prefrontal cortex
KW - Spontaneous up and down states
KW - Time integration
KW - Working memory
UR - http://www.scopus.com/inward/record.url?scp=84871717574&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871717574&partnerID=8YFLogxK
U2 - 10.1016/B978-008045046-9.01397-8
DO - 10.1016/B978-008045046-9.01397-8
M3 - Entry for encyclopedia/dictionary
AN - SCOPUS:84871717574
SN - 9780080450469
SP - 667
EP - 679
BT - Encyclopedia of Neuroscience
PB - Elsevier Ltd
ER -