Modeling of superbursts in neuronal cultures: Which synaptic and cellular mechanisms are required?
Tutkimustuotos › › vertaisarvioitu
|Otsikko||Neuroscience 2013; 43rd Annual Meeting, New Orleans, USA, November, 9-13, 2013|
|Tila||Julkaistu - 9 marraskuuta 2013|
|OKM-julkaisutyyppi||A4 Artikkeli konferenssijulkaisussa|
|Tapahtuma||Neuroscience 2013, Nov 9-13, San Diego, California - |
Kesto: 1 tammikuuta 2013 → …
|Conference||Neuroscience 2013, Nov 9-13, San Diego, California|
|Ajanjakso||1/01/13 → …|
In this work, we study computationally the contribution of different synaptic and cellular mechanisms to the network burst characteristics and to the emergence of the superbursts. In addition, the effect of the synaptic map is monitored by applying various types of connectivity graphs. We apply several point-neuron models, including a current-based leaky integrate-and-fire model (LIF) (Tsodyks et al., JNeurosci. 2000), a conductance-based leaky integrate-and-fire model (CLIF) (Compte et al., Cereb. Cortex 2000), and a Hodgkin-Huxley type of model (HH) (Golomb et al., JNeurophysiol. 2006). The synapses are modeled as chemical synapses with or without synaptic depression. Using the LIF model, the synaptic properties are extensively varied and their contributions to the network bursting are studied, both in excitatory-only and excitatory-inhibitory networks. Ceaseless bursts, in other words the runaway excitation, can be produced by recurrent networks without dynamical synapses, but for the cessation of the bursts either synaptic depression or strong enough recurrent inhibition has to be applied. In the CLIF model, the synaptic properties are held fixed, but the effect of inclusion of different excitatory synaptic currents (AMPA and NMDA) is monitored. The results are compared with the dynamics produced by the LIF and HH model. Using all above neuron models, we also monitor the effect of the network structure on the bursting activity. We show that certain structural classes promote the emergence of the superbursts, while certain structural classes more effectively restrict their lengths.