Self-sustained Asynchronous Irregular states and Up-Down states in thalamocortical networks of nonlinear integrate-and-fire neurons

In this talk I will present a paper [1] about a thalamocortical network model I will use as part of my research. I will start by providing a brief summary of the background and the research questions.

Our collaborators at Erasmus Medical Center, Rotterdam, The Netherlands, found that it is possible to stop epileptic absence seizures by exciting cerebellar nucleus (CN) neurons in a closed loop system (this work was done together with a former PhD student, Parimala Alva, [2]). In my PhD work, I will try to understand the mechanisms underlying the termination of seizures by optogenetic stimulation of CN neurons. For this purpose, I will use a thalamocortical network model of adaptive exponential integrate-and-fire neurons, as described in [1].

In the paper [1] the occurrence of Asynchronous Irregular (AI) states in thalamocortical networks of non-linear integrate-and-fire neurons has been investigated together with the role of spike-frequency adaptation. The main findings are that the thalamocortical networks can display AI states or Up-Down state dynamics, depending on the level of adaptation in cortical cells.

Date: 04/11/2016
Time: 16:00
Location: LB252

[1] Destexhe A. Self-sustained asynchronous irregular states and up-down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. J Comput Neurosci 2009; 27:493-506

[2] Kros et al, Cerebellar Output Controls Generalised Spike-and-Wave Discharge Occurrence, Ann Neurol, 2015

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