Robust persistent activity in neural fields with asymmetric connectivity
| Autor: | Wolfram Erlhagen, Cláudia Horta |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2006 |
| Předmět: |
Bistability
Cognitive Neuroscience media_common.quotation_subject Asymmetry 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Robustness (computer science) Control theory Position (vector) Attractor Neural field Statistical physics 030304 developmental biology Mathematics media_common 0303 health sciences Science & Technology Artificial neural network Basis (linear algebra) Working memory Symmetry (physics) Computer Science Applications Spatial orientation 030217 neurology & neurosurgery |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | Modeling studies have shown that recurrent interactions within neural networks are capable of self-sustaining non-uniform activity profiles. These patterns are thought to be the neural basis of working memory. However, the lack of robustness challenge this view as already small deviations from the assumed interaction symmetry destroy the attractor state. Here we analyze attractor states of a neural field model composed of bistable neurons. We show the existence of self-stabilized patterns that robustly represent the cue position in the presence of a substantial asymmetry in the connection profile. Using approximation techniques we derive an explicit expression for a threshold value describing the transition to a traveling activity wave. |
| Databáze: | OpenAIRE |
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