Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain.
Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech), Barcelona, Spain.
PLoS Comput Biol. 2022 May 18;18(5):e1009342. doi: 10.1371/journal.pcbi.1009342. eCollection 2022 May.
Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural circuits. The Communication Through Coherence (CTC) theory proposes that oscillations reflect rhythmic changes in excitability of the neuronal populations. Thus, populations need to be properly phase-locked so that input volleys arrive at the peaks of excitability of the receiving population to communicate effectively. Here, we present a modeling study to explore synchronization between neuronal circuits connected with unidirectional projections. We consider an Excitatory-Inhibitory (E-I) network of quadratic integrate-and-fire neurons modeling a Pyramidal-Interneuronal Network Gamma (PING) rhythm. The network receives an external periodic input from either one or two sources, simulating the inputs from other oscillating neural groups. We use recently developed mean-field models which provide an exact description of the macroscopic activity of the spiking network. This low-dimensional mean field model allows us to use tools from bifurcation theory to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We identify the conditions for optimal phase-locking and effective communication. We find that inputs with high coherence can entrain the network for a wider range of frequencies. Besides, faster oscillatory inputs than the intrinsic network gamma cycle show more effective communication than inputs with similar frequency. Our analysis further shows that the entrainment of the network by inputs with higher frequency is more robust to distractors, thus giving them an advantage to entrain the network and communicate effectively. Finally, we show that pulsatile inputs can switch between attended inputs in selective attention.
大脑中的宏观振荡被观察到参与许多认知任务，但它们的作用尚不完全清楚。振荡的一个被提议的功能是动态调节神经回路之间的通信。通过相干性的通信（CTC）理论提出，振荡反映了神经元群体兴奋性的节律变化。因此，群体需要适当的相位锁定，以便输入脉冲在接收群体兴奋性的峰值到达，从而有效地进行通信。在这里，我们提出了一个建模研究，以探索具有单向投影的神经元回路之间的同步。我们考虑一个由二次积分和点火神经元组成的兴奋性抑制性（E-I）网络，模拟了锥体细胞-中间神经元网络伽马（PING）节律。该网络从一个或两个来源接收外部周期性输入，模拟来自其他振荡神经元群体的输入。我们使用最近开发的平均场模型，该模型为尖峰网络的宏观活动提供了精确的描述。这个低维的平均场模型允许我们使用分叉理论的工具来识别输入和目标群体之间的锁定相位状态，作为输入的幅度、频率和相干性的函数。我们确定了最佳锁定和有效通信的条件。我们发现，具有高相干性的输入可以使网络在更宽的频率范围内被锁定。此外，比内在网络伽马周期更快的振荡输入比具有相似频率的输入具有更有效的通信。我们的分析还进一步表明，具有更高频率的输入对网络的锁定比具有更高频率的输入更稳健，因此它们具有使网络锁定并有效通信的优势。最后，我们表明，脉冲输入可以在选择性注意中在关注的输入之间切换。
