Limited spreading: How hierarchical networks prevent the transition to the epileptic state
| Autor: | Jennifer Simonotto, Marcus Kaiser |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Small-world network
Artificial neural network Hierarchy (mathematics) business.industry Computer science Topology (electrical circuits) Network topology Inhibitory postsynaptic potential Visual cortex medicine.anatomical_structure Cerebral cortex medicine Artificial intelligence business Neuroscience |
| Zdroj: | Modeling Phase Transitions in the Brain ISBN: 9781441907950 |
| DOI: | 10.1007/978-1-4419-0796-7_5 |
| Popis: | An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The latter case of large-scale activation is visible in the transition to the epileptic state. After describing the shift to large-scale synchronization we study the role of network topology on this transition. Whereas standard explanations for balanced activity involve populations of inhibitory neurons for limiting activity, we observe how network topology limits activity spreading. A random or small-world topology results in low or high levels of activation. In contrast, a cluster hierarchy based on neuroanatomical knowledge—from cortical clusters such as the visual cortex at the highest level, to individual columns at the lowest level—enables sustained activity in neural systems and prevents large-scale activation as observed during epileptic seizures. The containment of activation critically depends on the ratio of inter-cluster connections. Such topological inhibition by means of a modular hierarchy, in addition to neuronal inhibition, might help to maintain healthy levels of neural activity. |
| Databáze: | OpenAIRE |
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