Transition to Chaos in Random Networks with Cell-Type-Specific Connectivity
Abstract
In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of neural networks with cell-type-specific connectivity by extending the dynamic mean-field method and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyperexcitable neurons within the network can significantly increase the network's computational capacity by bringing it into the chaotic regime.
- Publication:
-
Physical Review Letters
- Pub Date:
- February 2015
- DOI:
- 10.1103/PhysRevLett.114.088101
- arXiv:
- arXiv:1407.2297
- Bibcode:
- 2015PhRvL.114h8101A
- Keywords:
-
- 87.18.Sn;
- 02.10.Yn;
- 05.90.+m;
- 87.19.lj;
- Neural networks;
- Matrix theory;
- Other topics in statistical physics thermodynamics and nonlinear dynamical systems;
- Neuronal network dynamics;
- Quantitative Biology - Neurons and Cognition;
- Condensed Matter - Disordered Systems and Neural Networks;
- Mathematics - Probability;
- Nonlinear Sciences - Chaotic Dynamics
- E-Print:
- Phys. Rev. Lett. 114, 088101 (2015)