The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks.
Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5967741?pdf=render |
| _version_ | 1818504646404079616 |
|---|---|
| author | Erik D Fagerholm Martin Dinov Thomas Knöpfel Robert Leech |
| author_facet | Erik D Fagerholm Martin Dinov Thomas Knöpfel Robert Leech |
| author_sort | Erik D Fagerholm |
| collection | DOAJ |
| description | Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality. |
| first_indexed | 2024-12-10T21:39:54Z |
| format | Article |
| id | doaj.art-a93f2688ba5741bcaf8deeba51e84751 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T21:39:54Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a93f2688ba5741bcaf8deeba51e847512022-12-22T01:32:32ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01135e019789310.1371/journal.pone.0197893The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks.Erik D FagerholmMartin DinovThomas KnöpfelRobert LeechLocal perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality.http://europepmc.org/articles/PMC5967741?pdf=render |
| spellingShingle | Erik D Fagerholm Martin Dinov Thomas Knöpfel Robert Leech The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. PLoS ONE |
| title | The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. |
| title_full | The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. |
| title_fullStr | The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. |
| title_full_unstemmed | The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. |
| title_short | The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks. |
| title_sort | characteristic patterns of neuronal avalanches in mice under anesthesia and at rest an investigation using constrained artificial neural networks |
| url | http://europepmc.org/articles/PMC5967741?pdf=render |
| work_keys_str_mv | AT erikdfagerholm thecharacteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT martindinov thecharacteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT thomasknopfel thecharacteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT robertleech thecharacteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT erikdfagerholm characteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT martindinov characteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT thomasknopfel characteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks AT robertleech characteristicpatternsofneuronalavalanchesinmiceunderanesthesiaandatrestaninvestigationusingconstrainedartificialneuralnetworks |