Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks
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Summary
By viewing permitted sets as memories stored in the synaptic connections, this work provides a formulation of long-term memory that is more general than the traditional perspective of fixed-point attractor networks.
- Type
- article
- Published
- 2003-02-27
- Cited by
- 250
- References
- 24
- OpenAlex
- https://openalex.org/W2115761837
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5989075
Keywords
Multistability, Mathematics, Fixed point, Attractor, Bounded function
References
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- Matrix analysis
- Nonlinear Programming
Cited by
- Dynamics analysis and analog associative memory of networks with LT neurons
- Simple Algorithms for Graph Partition Problems
- Exploration of the inter-areal cortico-cortical network of the macaque monkey
- A parsimonious computational model of visual target position encoding in the superior colliculus
- Project Adam: Building an Efficient and Scalable Deep Learning Training System
- Switched linear encoding with rectified linear autoencoders
- Contraction and partial contraction : a study of synchronization in nonlinear networks
- A Simple Message Passing Algorithm for Graph Partitioning Problems
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State
- Universal Memory Architectures for Autonomous Machines
- Topology and dynamics of the canonical circuit of cat V1
- Think co(mpletely)positive ! Matrix properties, examples and a clustered bibliography on copositive optimization
- Competitive Layer Model of Discrete-Time Recurrent Neural Networks with LT Neurons
- Neuronal variability: noise or part of the signal?
- Complete Convergence of Competitive Neural Networks with Different Time Scales
- Computation in Dynamically Bounded Asymmetric Systems
- Recurrent neuronal circuits in the neocortex.
- A Special Criteria to Globally Exponentially Stability for Discrete-Time Recurrent Neural Networks
- Selectivity and Stability via Dendritic Nonlinearity
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