Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Poster

Bursting behavior in sparse random networks of excitatory and inhibitory neurons

MPG-Autoren
/persons/resource/persons215926

Vinogradov,  O
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons173580

Levina,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Vinogradov, O., Sukenik, N., Moses, E., & Levina, A. (2018). Bursting behavior in sparse random networks of excitatory and inhibitory neurons. Poster presented at Bernstein Conference 2018, Berlin, Germany. doi:10.12751/nncn.bc2018.0177.


Zitierlink: http://hdl.handle.net/21.11116/0000-0002-470A-4
Zusammenfassung
Network bursting is the most common type of spontaneous activity of dissociated neuronal cultures [1,2]. The frequency, length, amplitude, and shape of bursts vary substantially depending on the culture preparation, age, environment, and cellular composition [3]. Most of the models proposed to explain network bursting explicitly included components that drive bursting behavior, such as feedback, adaptation, or synaptic fatigue [4,5,6,7]. However, it is not clear whether such specific components are a necessary prerequisite of bursting activity. In the current study, we show that bursting occurs as one of the spontaneous dynamical states of a sparse random network of excitatory and inhibitory leaky Integrate-and-fire neurons with delta synapses. This type of network models is one of the most simple candidates to study the behavior of dissociated neuronal cultures. Yet, previously described synchronous states of such network models are not reconcilable with experimental observations [8]. These states have a relatively high frequency of global oscillations or show only a small amount of network synchrony. We investigated the behavior of the model outside of the typically studied parameter-intervals and found that population bursting appears in networks with strong synapses and very slow external Poisson input. In this specific region of the parameters space, the system shows slow bistable dynamics. The network spontaneously fluctuates between almost quiescent asynchronous state and fast synchronous firing. This transition closely resembles the network bursting in neuronal cultures. Our model displays a variety of burst’s shapes, amplitudes, and frequencies, some of which exhibit a clear parameter dependence. For instance, we demonstrate that the frequency of bursts depends on the coupling strength and relative strength of inhibitory synapses. Finally, we show that this type of dynamics is preserved in networks of different sizes when the synaptic strength is scaled proportionally 1K√. Overall, our model suggests that the sparse random network of excitatory and inhibitory neurons can exhibit various types of network bursting activity on the mesoscopic level. This concept can further extend mechanistic understanding of the variability of bursting dynamics in living neuronal cultures.