Di Geronimo Claudio, Destexhe Alain, Di Volo Matteo
Université Claude Bernard Lyon 1, Institut National de la Santé et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France.
Dipartimento di Fisica, Universita di Firenze, Via G. Sansone 1, I-50019, Sesto Fiorentino (FI), Italy.
J Comput Neurosci. 2025 Apr 23. doi: 10.1007/s10827-025-00904-7.
We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABA and nonlinear slow GABA inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABA conductance. Optogenetic activation of interneurons and an increase of GABA conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABA conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABA synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABA synapses play a major role to stabilize the network from the emergence of epileptic seizures.
我们提出了一种用于具有快速γ-氨基丁酸（GABA）和基于非线性慢GABA抑制性电导突触的兴奋性和抑制性神经元的脉冲神经网络的平均场模型。该平均场模型可以预测网络在异步不规则状态下对外部刺激的自发和诱发反应。对于足够强的GABA电导，该模型显示出θ振荡。中间神经元的光遗传学激活和GABA电导的增加对模型中γ振荡的出现产生了相反的影响。与神经网络的直接数值模拟和实验数据一致，平均场模型预测GABA电导的增加会减少γ振荡。此外，GABA突触的慢动力学调节了平均场模型中瞬态γ振荡（即γ爆发）的出现和持续时间。最后，我们表明非线性GABA突触在稳定网络以防癫痫发作的出现方面起着主要作用。
