Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties
Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity. These studies have been carried out at multiple spatial-temporal scale levels, including...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2022.905807/full |
| _version_ | 1811341100338642944 |
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| author | Sou Nobukawa Sou Nobukawa |
| author_facet | Sou Nobukawa Sou Nobukawa |
| author_sort | Sou Nobukawa |
| collection | DOAJ |
| description | Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity. These studies have been carried out at multiple spatial-temporal scale levels, including spikes at the neural microcircuit level, neural activity at the intra-brain regional level, and neural interactions at the whole-brain network level. One of the structural and functional neural characteristics widely observed among large spatial-temporal scale ranges is long-tail distribution, typified as power-low distribution, gamma distribution, and log-normal distribution. In particular, long-tailed distributions found in excitatory postsynaptic potentials (EPSP) induce various types of neural dynamics and functions. We reviewed recent studies on neural dynamics produced by the structural long-tailed characteristics of brain neural networks. In particular, the spiking neural network with a log-normal EPSP distribution was first introduced for the essential factors to produce spontaneous activity and was extended and utilized for studies on the association of neural dynamics with the network topology depending on EPSP amplitude. Furthermore, the characteristics of the response to a steady stimulus and its dependence on E/I balance, which are widely observed under pathological conditions, were described by the spiking neural networks with EPSP long-tailed distribution. Moreover, this spiking neural network has been utilized in modeling studies of mutual interactions among local microcircuit circuits. In future studies, the implementation of more global brain network architectures in modeling studies might reveal the mechanisms by which brain dynamics and brain functions emerge from the whole brain network architecture. |
| first_indexed | 2024-04-13T18:51:41Z |
| format | Article |
| id | doaj.art-56d6c0b97f3a47ccafaa8744f720ad7a |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-13T18:51:41Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-56d6c0b97f3a47ccafaa8744f720ad7a2022-12-22T02:34:24ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872022-05-01810.3389/fams.2022.905807905807Long-Tailed Characteristics of Neural Activity Induced by Structural Network PropertiesSou Nobukawa0Sou Nobukawa1Department of Computer Science, Chiba Institute of Technology, Narashino, JapanDepartment of Preventive Intervention for Psychiatric Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Tokyo, JapanOver the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity. These studies have been carried out at multiple spatial-temporal scale levels, including spikes at the neural microcircuit level, neural activity at the intra-brain regional level, and neural interactions at the whole-brain network level. One of the structural and functional neural characteristics widely observed among large spatial-temporal scale ranges is long-tail distribution, typified as power-low distribution, gamma distribution, and log-normal distribution. In particular, long-tailed distributions found in excitatory postsynaptic potentials (EPSP) induce various types of neural dynamics and functions. We reviewed recent studies on neural dynamics produced by the structural long-tailed characteristics of brain neural networks. In particular, the spiking neural network with a log-normal EPSP distribution was first introduced for the essential factors to produce spontaneous activity and was extended and utilized for studies on the association of neural dynamics with the network topology depending on EPSP amplitude. Furthermore, the characteristics of the response to a steady stimulus and its dependence on E/I balance, which are widely observed under pathological conditions, were described by the spiking neural networks with EPSP long-tailed distribution. Moreover, this spiking neural network has been utilized in modeling studies of mutual interactions among local microcircuit circuits. In future studies, the implementation of more global brain network architectures in modeling studies might reveal the mechanisms by which brain dynamics and brain functions emerge from the whole brain network architecture.https://www.frontiersin.org/articles/10.3389/fams.2022.905807/fullexcitatory postsynaptic potentiallog-normal distributionspiking neural networkstochastic resonancesynchronization |
| spellingShingle | Sou Nobukawa Sou Nobukawa Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties Frontiers in Applied Mathematics and Statistics excitatory postsynaptic potential log-normal distribution spiking neural network stochastic resonance synchronization |
| title | Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties |
| title_full | Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties |
| title_fullStr | Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties |
| title_full_unstemmed | Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties |
| title_short | Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties |
| title_sort | long tailed characteristics of neural activity induced by structural network properties |
| topic | excitatory postsynaptic potential log-normal distribution spiking neural network stochastic resonance synchronization |
| url | https://www.frontiersin.org/articles/10.3389/fams.2022.905807/full |
| work_keys_str_mv | AT sounobukawa longtailedcharacteristicsofneuralactivityinducedbystructuralnetworkproperties AT sounobukawa longtailedcharacteristicsofneuralactivityinducedbystructuralnetworkproperties |