Spatially localized cluster solutions in inhibitory neural networks
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Date
2021-06
Authors
Ryu, Hwayeon
Miller, Jennifer
Teymuroglu, Zeynep
Wang, Xueying
Booth, Victoria
Campbell, Sue Ann
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Neurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make stronger connections with their th nearest neighbors on each side and weaker connections with closer neighbors. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.
Description
The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.mbs.2021.108591. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
inhibitory networks, connectivity, clusters, phase model