Phase Resetting Curves Predict Network Activity in Networks of Neural Oscillators
نویسندگان
چکیده
منابع مشابه
Phase-resetting curves determine synchronization, phase locking, and clustering in networks of neural oscillators.
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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Pulse-coupled phase oscillators have been utilized in a variety of contexts. Motivated by neuroscience, we study a network of pulse-coupled phase oscillators receiving independent and correlated noise. An additional physiological attribute, heterogeneity, is incorporated in the phase resetting curve (PRC), which is a vital entity for modeling the biophysical dynamics of oscillators. An accurate...
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Biological oscillations, despite their vast range of periodicities, can be described mathematically by stable limit cycles. Therefore, a general theory characterizing the effects of perturbations applied to such limit cycles allows predictions of qualitative features of a particular oscillation subject to perturbation. In this chapter, we summarize this topological approach and discuss ways in ...
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Existence and stability criteria for harmonic locking modes were derived for two reciprocally pulse coupled oscillators based on their first and second order phase resetting curves. Our theoretical methods are general in the sense that no assumptions about the strength of coupling, type of synaptic coupling, and model are made. These methods were then tested using two reciprocally inhibitory Wa...
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ژورنال
عنوان ژورنال: Frontiers in Systems Neuroscience
سال: 2009
ISSN: 1662-5137
DOI: 10.3389/conf.neuro.06.2009.03.113