Centre for Cognition and Decision Making, HSE University, Moscow, Russia.
Group for Neural Theory, LNC2 INSERM U960, Départment d'Études Cognitives, École Normale Supérieure, PSL Research Université, Paris, France.
Front Neural Circuits. 2021 Apr 21;15:647944. doi: 10.3389/fncir.2021.647944. eCollection 2021.
According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
根据工作记忆（WM）的机制理论，信息作为皮质神经网络的刺激依赖性持久尖峰活动而保留。然而，这种活动与在 WM 任务期间观察到的振荡模式变化有何关联，在很大程度上仍是一个悬而未决的问题。我们探讨了输入伽马带振荡和噪声对 WM 的几种发放率模型的动力学的联合影响。所考虑的模型具有亚稳定的活跃状态，即它们表现出持久的短暂刺激后发放率升高。我们从一个单个的兴奋-抑制电路开始，并证明伽马带或噪声输入都可以稳定活跃状态，从而支持 WM 保留。然后我们考虑一个具有兴奋性相互耦合的两个电路系统。我们发现，快速耦合允许共同噪声比独立噪声更好地稳定活跃状态，并且同相伽马输入比反相输入对这种效应的放大作用更强。最后，我们考虑了一个由两个集群组成的多电路系统，每个集群包含一组接收共同噪声输入的电路和一组接收独立噪声输入的电路。每个集群都与自己的局部伽马发生器相关联，因此所有电路都以相同的相位接收伽马带输入。我们发现，与独立噪声组相比，伽马带输入可以使“共同噪声”组的活动有差异地稳定下来。如果集群间的连接很快，当伽马带输入以相同的相位而不是反相传递到集群时，这种效果更为明显。假设共同噪声来自于大规模分布式 WM 表示，我们的结果表明，局部伽马振荡可以稳定该表示的相应部分的活动，而对于快速长程连接和同步伽马振荡，其效果更强。
