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- Open Access
Cross-talk and transitions between multiple environments in an attractor neural network model of the hippocampus
© Rosay and Monasson; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Firing Rate
- Neural Network Model
- Dynamical Transition
- External Input
- Spatial Representation
Place cells are neurons in the hippocampus whose activity depends on the animal's location in space and are therefore thought to be crucial for spatial representation . Based on the assumption that CA3 works as an attractor neural network  models have shown that spatially-localized attractors, corresponding to different 'environments' or 'spatial maps', can be encoded in one network [2, 3]. Transitions and cross-talks between attractors coding for different maps remain, however, poorly understood.
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