Book chapter
Clustering in Inhibitory Neural Networks with Nearest Neighbor Coupling
Applications of Dynamical Systems in Biology and Medicine, pp.99-121
The IMA Volumes in Mathematics and its Applications, Springer New York
Handle:
https://hdl.handle.net/2376/104860
Abstract
We investigate the clustering dynamics of a network of inhibitory interneurons, where each neuron is connected to some set of its neighbors. We use phase model analysis to study the existence and stability of cluster solutions. In particular, we describe cluster solutions which exist for any type of oscillator, coupling and connectivity. We derive conditions for the stability of these solutions in the case where each neuron is coupled to its two nearest neighbors on each side. We apply our analysis to show that changing the connection weights in the network can change the stability of solutions in the inhibitory network. Numerical simulations of the full network model confirm and supplement our theoretical analysis. Our results support the hypothesis that cluster solutions may be related to the formation of neural assemblies.
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Details
- Title
- Clustering in Inhibitory Neural Networks with Nearest Neighbor Coupling
- Creators
- Jennifer MillerHwayeon RyuZeynep TeymurogluXueying WangVictoria BoothSue Ann Campbell
- Publication Details
- Applications of Dynamical Systems in Biology and Medicine, pp.99-121
- Academic Unit
- Mathematics and Statistics, Department of
- Series
- The IMA Volumes in Mathematics and its Applications
- Publisher
- Springer New York; New York, NY
- Identifiers
- 99900546739901842
- Language
- English
- Resource Type
- Book chapter