Stability of neuronal networks with homeostatic regulation
Neurons are equipped with homeostatic mechanisms that counteract long-term perturbations of their average activity and thereby keep neurons in a healthy and information-rich operating regime. While homeostasis is believed to be crucial for neural function, a systematic analysis of homeostatic contro...
| Main Authors: | , , , , |
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| Format: | Article |
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Public Library of Science
2015
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| Online Access: | https://eprints.nottingham.ac.uk/49630/ |
| _version_ | 1848798041533841408 |
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| author | Harnack, Daniel Pelko, Miha Chaillet, Antoine Chitour, Yacine van Rossum, Mark C.W. |
| author_facet | Harnack, Daniel Pelko, Miha Chaillet, Antoine Chitour, Yacine van Rossum, Mark C.W. |
| author_sort | Harnack, Daniel |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Neurons are equipped with homeostatic mechanisms that counteract long-term perturbations of their average activity and thereby keep neurons in a healthy and information-rich operating regime. While homeostasis is believed to be crucial for neural function, a systematic analysis of homeostatic control has largely been lacking. The analysis presented here analyses the necessary conditions for stable homeostatic control. We consider networks of neurons with homeostasis and show that homeostatic control that is stable for single neurons, can destabilize activity in otherwise stable recurrent networks leading to strong non-abating oscillations in the activity. This instability can be prevented by slowing down the homeostatic control. The stronger the network recurrence, the slower the homeostasis has to be. Next, we consider how non-linearities in the neural activation function affect these constraints. Finally, we consider the case that homeostatic feedback is mediated via a cascade of multiple intermediate stages. Counter-intuitively, the addition of extra stages in the homeostatic control loop further destabilizes activity in single neurons and networks. Our theoretical framework for homeostasis thus reveals previously unconsidered constraints on homeostasis in biological networks, and identifies conditions that require the slow time-constants of homeostatic regulation observed experimentally. |
| first_indexed | 2025-11-14T20:13:28Z |
| format | Article |
| id | nottingham-49630 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:28Z |
| publishDate | 2015 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-496302020-05-04T17:13:30Z https://eprints.nottingham.ac.uk/49630/ Stability of neuronal networks with homeostatic regulation Harnack, Daniel Pelko, Miha Chaillet, Antoine Chitour, Yacine van Rossum, Mark C.W. Neurons are equipped with homeostatic mechanisms that counteract long-term perturbations of their average activity and thereby keep neurons in a healthy and information-rich operating regime. While homeostasis is believed to be crucial for neural function, a systematic analysis of homeostatic control has largely been lacking. The analysis presented here analyses the necessary conditions for stable homeostatic control. We consider networks of neurons with homeostasis and show that homeostatic control that is stable for single neurons, can destabilize activity in otherwise stable recurrent networks leading to strong non-abating oscillations in the activity. This instability can be prevented by slowing down the homeostatic control. The stronger the network recurrence, the slower the homeostasis has to be. Next, we consider how non-linearities in the neural activation function affect these constraints. Finally, we consider the case that homeostatic feedback is mediated via a cascade of multiple intermediate stages. Counter-intuitively, the addition of extra stages in the homeostatic control loop further destabilizes activity in single neurons and networks. Our theoretical framework for homeostasis thus reveals previously unconsidered constraints on homeostasis in biological networks, and identifies conditions that require the slow time-constants of homeostatic regulation observed experimentally. Public Library of Science 2015-07-08 Article PeerReviewed Harnack, Daniel, Pelko, Miha, Chaillet, Antoine, Chitour, Yacine and van Rossum, Mark C.W. (2015) Stability of neuronal networks with homeostatic regulation. PLoS computational biology, 11 (7). e1004357. ISSN 1553-7358 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004357 doi:10.1371/journal.pcbi.1004357 doi:10.1371/journal.pcbi.1004357 |
| spellingShingle | Harnack, Daniel Pelko, Miha Chaillet, Antoine Chitour, Yacine van Rossum, Mark C.W. Stability of neuronal networks with homeostatic regulation |
| title | Stability of neuronal networks with homeostatic regulation |
| title_full | Stability of neuronal networks with homeostatic regulation |
| title_fullStr | Stability of neuronal networks with homeostatic regulation |
| title_full_unstemmed | Stability of neuronal networks with homeostatic regulation |
| title_short | Stability of neuronal networks with homeostatic regulation |
| title_sort | stability of neuronal networks with homeostatic regulation |
| url | https://eprints.nottingham.ac.uk/49630/ https://eprints.nottingham.ac.uk/49630/ https://eprints.nottingham.ac.uk/49630/ |