论文标题
具有短期可塑性的尖峰神经网络中海马重播和亚稳定性的介观描述
Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
论文作者
论文摘要
神经活动功能相关模式的自下而上模型在神经元动力学和计算之间提供了明确的联系。功能活动模式的一个主要示例是海马重放，这对于记忆巩固至关重要。在神经记录中，重播事件与低活动状态之间的切换表明了亚稳态的神经回路动力学。由于亚稳定性归因于噪声和/或缓慢的疲劳机制，因此我们提出了一种简洁的介观模型，这两者都会说明两者。至关重要的是，我们的模型是自下而上的：它是从有限尺寸的线性 - 非线性泊松神经元具有短期突触抑制的线性非线性泊松神经元网络的动力学得出的。因此，噪声与尖峰噪声和网络大小明确链接，并且疲劳与突触动力学明确链接。为了推导中菌模型，我们首先考虑均匀的尖峰神经网络，并遵循吉莱斯皮（“化学兰格文方程”）的时间粗粒方法，可以自然地解释为随机神经质量模型。 Langevin方程在计算上是廉价的，可以通过相位平面分析对经典设置（人口尖峰和较高状态动力学）中亚稳态动态进行彻底研究。这种随机神经质量模型是我们重播的介观模型的基本组成部分。我们表明，我们的模型忠实地捕捉了单个重播轨迹的随机性。此外，与确定性的Romani-Tsodyks的位置细胞动力学模型相比，它在重播事件的内容，方向和时机方面表现出更高的可变性，与生物学证据兼容，并且在功能上是可取的。这种变异性是一种新的动力学状态的乘积，在该型号中，有限大小波动与局部疲劳之间的复杂相互作用出现了亚电率。
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity pattern is hippocampal replay, which is critical for memory consolidation. The switchings between replay events and a low-activity state in neural recordings suggests metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to spike noise and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesosocpic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie ("chemical Langevin equation"), which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down states dynamics) by means of phase-plane analysis. This stochastic neural mass model is the basic component of our mesoscopic model for replay. We show that our model faithfully captures the stochastic nature of individual replayed trajectories. Moreover, compared to the deterministic Romani-Tsodyks model of place cell dynamics, it exhibits a higher level of variability in terms of content, direction and timing of replay events, compatible with biological evidence and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.
