Phase-response curves and synchronized neural networks
نویسندگان
چکیده
منابع مشابه
Phase-response curves and synchronized neural networks.
We review the principal assumptions underlying the application of phase-response curves (PRCs) to synchronization in neuronal networks. The PRC measures how much a given synaptic input perturbs spike timing in a neural oscillator. Among other applications, PRCs make explicit predictions about whether a given network of interconnected neurons will synchronize, as is often observed in cortical st...
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Neural-network dynamics frequently organize in assemblies of synchronized neurons that are thought to encode and store sensory information. We have investigated the mechanisms leading to the emergence of these neural assemblies with models of coupled oscillators. In particular, we used experimentally estimated phase-resetting curves (PRC) of real neurons (mitral cells) to realistically describe...
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We prove that a group of injection-locked oscillators, each modelled using a nonlinear phase macromodel, responds as a single oscillator to small external perturbations. More precisely, we show that any group of injection-locked oscillators has a single effective PRC [1] or PPV [2], [3] that characterises its phase/timing response to small external perturbations. This result constitutes a found...
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Networks of model neurons were constructed and their activity was predicted using an iterated map based solely on the phase-resetting curves (PRCs). The predictions were quite accurate provided that the resetting to simultaneous inputs was calculated using the sum of the simultaneously active conductances, obviating the need for weak coupling assumptions. Fully synchronous activity was observed...
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ژورنال
عنوان ژورنال: Philosophical Transactions of the Royal Society B: Biological Sciences
سال: 2010
ISSN: 0962-8436,1471-2970
DOI: 10.1098/rstb.2009.0292