Notions of associative memory and sparse coding
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Memory and learning of sequential patterns by nonmonotone neural networks
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Matrix computations (3rd ed.)
On the storage capacity of nonlinear neural networks
Neural Networks
Associative memory with a sparse encoding mechanism for storing correlated patterns
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
The gene expression matrix: towards the extraction of genetic network architectures
Proceedings of the second world congress on Nonlinear analysts: part 3
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Knowledge discovery and emergent complexity in bioinformatics
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
The identification of dynamic gene-protein networks
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
On sparse representations in arbitrary redundant bases
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
In this paper we study the potential of gene-protein interaction networks to store input-output patterns. The central question in this study concerns the memory capacity of a network of a given number of genes and proteins, which interact according to a linear state space model with external inputs. Here it is assumed that to a certain combination of inputs there exists an optimal state of the system, i.e., values of the gene expressions and protein levels, that has been attained externally, e.g., through evolutionary learning. Given such a set of learned optimal input-output patterns, the design question here is to find a sparse and hierarchical network structure for the gene-protein interactions and the gene-input couplings. This problem is formulated as an optimization problem in a linear programming setting. Numerical analysis shows that there are clear scale-invariant continuous second-order phase transitions for the network sparsity as the number of patterns increases. These phase transitions divide the system in three regions with different memory characteristics. It is possible to formulate simple scaling rules for the behavior of the network sparsity. Finally, numerical experiments show that these patterns are stable within a certain finite range around the patterns.