发表机构
University of Minnesota（明尼苏达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出具有兴奋性、PV和SST抑制性亚型的随机神经场模型，分析凸起稳定性与游走，发现SST连接宽度增强稳定性并降低记忆扩散。
AI 中文摘要
工作记忆中连续变量信息的维持被认为依赖于皮层活动的持续模式。在延迟估计任务中，神经活动可以形成局部活动峰值，即“凸起”，其位置跟踪记忆的变量。这种活动可由连续吸引子神经场模型很好地描述，但大多数现有模型将皮层抑制简化为单一同质群体。在此，我们引入一个具有不同兴奋性、表达小清蛋白（PV）和表达生长抑素（SST）群体的随机神经场模型，以研究抑制性亚型结构如何塑造持续活动。利用Heaviside发放率近似，我们推导出稳态凸起解，并将其线性稳定性分解为独立的平移和缩放模式。我们表明，群体阈值和抑制性时间常数决定凸起稳定性及稳定性丧失的机制，而抑制性连接强度和空间尺度显著重塑稳定参数区域。特别是，更广泛的SST连接促进稳定的凸起状态。最后，我们推导出噪声驱动凸起游走的有效扩散系数，并表明增加SST空间足迹会降低记忆扩散速率。总之，这些结果展示了抑制性亚型结构如何塑造连续吸引子记忆的确定性稳定性和随机精度。
英文摘要
The maintenance of continuous variable information in working memory is thought to rely on persistent patterns of cortical activity. In delayed-estimation tasks, neural activity can form localized activity peaks, or ``bumps,'' whose positions track the remembered variable. Such activity is well described by continuous-attractor neural field models, but most existing models collapse cortical inhibition into a single homogeneous population. Here, we introduce a stochastic neural field model with distinct excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SST) populations to examine how inhibitory subtype structure shapes persistent activity. Using a Heaviside firing-rate approximation, we derive stationary bump solutions and reduce their linear stability to separate shifting and scaling modes. We show that population thresholds and inhibitory timescales determine both bump stability and the mechanism by which stability is lost, while inhibitory connection strengths and spatial scales substantially reshape the stable parameter region. In particular, broader SST connectivity promotes stable bump states. Finally, we derive an effective diffusion coefficient for noise-driven bump wandering and show that increasing the SST spatial footprint reduces the rate of memory diffusion. Together, these results demonstrate how inhibitory subtype structure can shape both the deterministic stability and stochastic precision of continuous-attractor memories.
