Structural constraints on the emergence of oscillations in multi-population neural networks
Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not p...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-03-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/88777 |
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| author | Jie Zang Shenquan Liu Pascal Helson Arvind Kumar |
| author_facet | Jie Zang Shenquan Liu Pascal Helson Arvind Kumar |
| author_sort | Jie Zang |
| collection | DOAJ |
| description | Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings. |
| format | Article |
| id | doaj-art-deae3bd1462842d28bd24e33e56fc8f1 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2024-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-deae3bd1462842d28bd24e33e56fc8f12024-11-11T14:47:11ZengeLife Sciences Publications LtdeLife2050-084X2024-03-011210.7554/eLife.88777Structural constraints on the emergence of oscillations in multi-population neural networksJie Zang0https://orcid.org/0000-0003-2655-3343Shenquan Liu1Pascal Helson2https://orcid.org/0000-0002-2877-3705Arvind Kumar3https://orcid.org/0000-0002-8044-9195School of Mathematics, South China University of Technology, Guangzhou, China; Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, SwedenSchool of Mathematics, South China University of Technology, Guangzhou, ChinaDivision of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Science for Life Laboratory, Stockholm, SwedenDivision of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Science for Life Laboratory, Stockholm, SwedenOscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.https://elifesciences.org/articles/88777oscillationsneural networksbasal ganglianetwork structurenetwork dynamics |
| spellingShingle | Jie Zang Shenquan Liu Pascal Helson Arvind Kumar Structural constraints on the emergence of oscillations in multi-population neural networks eLife oscillations neural networks basal ganglia network structure network dynamics |
| title | Structural constraints on the emergence of oscillations in multi-population neural networks |
| title_full | Structural constraints on the emergence of oscillations in multi-population neural networks |
| title_fullStr | Structural constraints on the emergence of oscillations in multi-population neural networks |
| title_full_unstemmed | Structural constraints on the emergence of oscillations in multi-population neural networks |
| title_short | Structural constraints on the emergence of oscillations in multi-population neural networks |
| title_sort | structural constraints on the emergence of oscillations in multi population neural networks |
| topic | oscillations neural networks basal ganglia network structure network dynamics |
| url | https://elifesciences.org/articles/88777 |
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