Name
Abstract | ||
|---|---|---|
Learning in the mammalian brain is commonly modeled through changing synaptic connections in cortical networks. Dynamical brain models indicate that learning leads to the formation of limit cycle oscillations across cortical areas and that the oscillatory regimes re-emerge when the learnt input is presented to the system. In this work, learning is modeled using a graph-theoretical model, which captures salient characteristics of the learning process. We introduce a random graph that combines a torus with lattice edges and additional random edges, which have power law length distribution. On this graph, we consider bootstrap percolation with excitatory and inhibitory vertices. Theoretical and numerical studies indicate the presence of various dynamical regimes on these graphs. Here, the transitions between fixed-point and limit cycle attractors are analyzed. We link this transition to changes in cortical networks during category learning, which have been observed in animal experiments using electro-cortiograph (ECoG) arrays over sensory cortices. We discuss how learning leads to categorization and strategy formation, and how the theoretical modeling results can be used for designing learning and adaptation in computationally aware intelligent machines. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Attractor,Categorization,Random graph,Vertex (geometry),Computer science,Concept learning,Limit cycle,Artificial intelligence,Power law,Cybernetics,Machine learning |
DocType | ISSN | Citations |
Conference | 1062-922X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Kozma | 1 | 21 | 10.20 |
| Yury Sokolov | 2 | 50 | 2.57 |
| Marko Puljic | 3 | 43 | 6.35 |
| Sanqing Hu | 4 | 452 | 42.72 |
| M. Ruszinkó | 5 | 230 | 35.16 |