ArticleFrontiers in computational neuroscience2012
Population spikes in cortical networks during different functional states.
Article in Frontiers in computational neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- Feature Detection in Visual Cortex during Different Functional States.Frontiers in computational neuroscience · 2017Article
- Network Anisotropy Trumps Noise for Efficient Object Coding in Macaque Inferior Temporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
- A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.Journal of computational neuroscience · 2015Article
- Emergent spike patterns in neuronal populations.Journal of computational neuroscience · 2015Article
- Editorial: State-dependent brain computation.Frontiers in computational neuroscience · 2015Article
- Adaptation to prolonged neuromodulation in cortical cultures: an invariable return to network synchrony.BMC biology · 2014Article
- A network model of the periodic synchronization process in the dynamics of calcium concentration in GnRH neurons.Journal of mathematical neuroscience · 2013Article
- Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity.Frontiers in computational neuroscience · 2013Article
- Neuronal avalanches differ from wakefulness to deep sleep--evidence from intracranial depth recordings in humans.PLoS computational biology · 2013Article
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2 authors.
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Abstract
Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing [Population Spikes (PS)] can be generated in homogenous recurrent neural networks with short-term synaptic depression (STD). Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.
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