Title: Multiscale motion and deformation of bumps in stochastic neural fields with dynamic connectivity Show Chinese title

Title: 随机神经场中具有动态连接的尖峰的多尺度运动和变形

Abstract: The distinct timescales of synaptic plasticity and neural activity dynamics play an important role in the brain's learning and memory systems. Activity-dependent plasticity reshapes neural circuit architecture, determining spontaneous and stimulus-encoding spatiotemporal patterns of neural activity. Neural activity bumps maintain short term memories of continuous parameter values, emerging in spatially-organized models with short term excitation and long-range inhibition. Previously, we demonstrated nonlinear Langevin equations derived using an interface method accurately describe the dynamics of bumps in continuum neural fields with separate excitatory/inhibitory populations. Here we extend this analysis to incorporate effects of slow short term plasticity that modifies connectivity described by an integral kernel. Linear stability analysis adapted to these piecewise smooth models with Heaviside firing rates further indicate how plasticity shapes bumps' local dynamics. Facilitation (depression), which strengthens (weakens) synaptic connectivity originating from active neurons, tends to increase (decrease) stability of bumps when acting on excitatory synapses. The relationship is inverted when plasticity acts on inhibitory synapses. Multiscale approximations of the stochastic dynamics of bumps perturbed by weak noise reveal the plasticity variables evolve to slowly diffusing and blurred versions of that arising in the stationary solution. Nonlinear Langevin equations associated with bump positions or interfaces coupled to slowly evolving projections of plasticity variables accurately describe the wandering of bumps underpinned by these smoothed synaptic efficacy profiles. Show Chinese abstract

Abstract: 突触可塑性和神经活动动态的不同时间尺度在大脑的学习和记忆系统中起着重要作用。 活动依赖的可塑性重塑神经回路结构，决定了神经活动的自发和刺激编码的时空模式。 神经活动的局部激活维持连续参数值的短期记忆，在具有短期兴奋和长程抑制的空间组织模型中出现。 此前，我们证明了使用界面方法推导出的非线性朗之万方程能准确描述具有分离兴奋/抑制种群的连续神经场中局部激活的动力学。 在这里，我们将这种分析扩展到包括慢速短期可塑性的影响，该可塑性通过积分核描述连接性的变化。 适应于分段光滑模型和阶跃函数发放率的线性稳定性分析进一步表明可塑性如何塑造局部激活的局部动力学。 增强（抑制），即加强（减弱）来自活跃神经元的突触连接，当作用于兴奋性突触时，会增加（减少）局部激活的稳定性。 当可塑性作用于抑制性突触时，这种关系是相反的。 对受弱噪声扰动的局部激活随机动力学的多尺度近似揭示了可塑性变量演变为静止解中出现的缓慢扩散和模糊版本。 与局部激活位置或界面相关且耦合到可塑性变量缓慢演化投影的非线性朗之万方程能够准确描述由这些平滑突触效能轮廓支撑的局部激活的游动。