Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
Phys Rev E. 2018 Jan;97(1-1):012209. doi: 10.1103/PhysRevE.97.012209.
Gamma oscillations are thought to play an important role in brain function. Interneuron gamma (ING) and pyramidal interneuron gamma (PING) mechanisms have been proposed as generation mechanisms for these oscillations. However, the relation between the generation mechanisms and the dynamical properties of the gamma oscillation are still unclear. Among the dynamical properties of the gamma oscillation, the phase response function (PRF) is important because it encodes the response of the oscillation to inputs. Recently, the PRF for an inhibitory population of modified theta neurons that generate an ING rhythm was computed by the adjoint method applied to the associated Fokker-Planck equation (FPE) for the model. The modified theta model incorporates conductance-based synapses as well as the voltage and current dynamics. Here, we extended this previous work by creating an excitatory-inhibitory (E-I) network using the modified theta model and described the population dynamics with the corresponding FPE. We conducted a bifurcation analysis of the FPE to find parameter regions which generate gamma oscillations. In order to label the oscillatory parameter regions by their generation mechanisms, we defined ING- and PING-type gamma oscillation in a mathematically plausible way based on the driver of the inhibitory population. We labeled the oscillatory parameter regions by these generation mechanisms and derived PRFs via the adjoint method on the FPE in order to investigate the differences in the responses of each type of oscillation to inputs. PRFs for PING and ING mechanisms are derived and compared. We found the amplitude of the PRF for the excitatory population is larger in the PING case than in the ING case. Finally, the E-I population of the modified theta neuron enabled us to analyze the PRFs of PING-type gamma oscillation and the entrainment ability of E and I populations. We found a parameter region in which PRFs of E and I are both purely positive in the case of PING oscillations. The different entrainment abilities of E and I stimulation as governed by the respective PRFs was compared to direct simulations of finite populations of model neurons. We find that it is easier to entrain the gamma rhythm by stimulating the inhibitory population than by stimulating the excitatory population as has been found experimentally.
伽马振荡被认为在大脑功能中起着重要作用。神经元γ(ING)和锥体神经元γ(PING)机制已被提出作为这些振荡的产生机制。然而,产生机制与伽马振荡的动力学特性之间的关系仍然不清楚。在伽马振荡的动力学特性中,相位响应函数(PRF)很重要,因为它编码了振荡对输入的响应。最近,通过应用于相关福克-普朗克方程(FPE)的伴随方法,计算了生成 ING 节律的改良 theta 神经元抑制群体的 PRF。改良 theta 模型结合了基于电导的突触以及电压和电流动力学。在这里,我们通过使用改良 theta 模型创建兴奋性-抑制性(E-I)网络来扩展之前的工作,并使用相应的 FPE 描述群体动力学。我们对 FPE 进行了分岔分析,以找到产生伽马振荡的参数区域。为了通过抑制群体的驱动以数学上合理的方式对振荡参数区域进行标记,我们根据 ING 和 PING 型伽马振荡的产生机制对其进行了定义。我们通过这些生成机制对振荡参数区域进行了标记,并通过伴随方法在 FPE 上推导了 PRF,以研究每种类型的振荡对输入的响应差异。推导并比较了 PING 和 ING 机制的 PRF。我们发现,在 PING 情况下,兴奋性群体的 PRF 幅度大于 ING 情况。最后,改良 theta 神经元的 E-I 群体使我们能够分析 PING 型伽马振荡的 PRF 和 E 和 I 群体的同步能力。我们发现,在 PING 振荡的情况下,E 和 I 的 PRF 都是纯正的参数区域。分别由各自的 PRF 控制的 E 和 I 刺激的不同同步能力与模型神经元有限群体的直接模拟进行了比较。我们发现,通过刺激抑制群体来同步伽马节律比通过刺激兴奋群体更容易,这与实验中发现的情况一致。