Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field
Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field
复制标题
随机神经场中明显的兴奋性和抑制性凹凸游走
DOI：
10.1137/22m1482329
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发表时间：
2022
影响因子：
2.1
通讯作者：
Kilpatrick, Zachary P.
中科院分区：
文献类型：
--
作者：
Cihak, Heather L.;Eissa, Tahra L.;Kilpatrick, Zachary P.
局部持续皮质神经活动是参数工作记忆的经过验证的神经基础。这种活动“颠簸”代表了提示在几秒钟内的连续位置。锥体（兴奋性（E））和中间神经元（抑制性（I））亚群表现出调节的活动颠簸，将神经动力学与记忆回忆中观察到的行为不准确联系起来。然而，许多凹凸吸引子模型将这些亚群折叠成单个联合 E/I（横向抑制）群体，并且没有考虑群体间神经结构和噪声相关性的作用。这两个因素都很有可能影响这些波动的随机动态，最终形成行为反应差异。在我们的研究中，我们考虑具有独立 E/I 群体的神经场模型，并利用渐近分析来推导描述 E/I 碰撞相互作用的非线性 Langevin 系统。虽然 E 凸块吸引 I 凸块，但 I 凸块稳定，但也可以排斥 E 凸块，这可能会导致当两个凸块都受到干扰时，长时间的松弛动态。此外，亚群内部和亚群之间的噪声相关性结构强烈影响了碰撞位置的变化。令人惊讶的是，较高的群体间相关性会减少方差。
Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity “bumps” represent the continuous location of a cue over several seconds. Pyramidal (excitatory (E)) and interneuronal (inhibitory (I)) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint E/I(lateral inhibitory) population and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate E/I populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E/I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.
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DOI：
10.1073/pnas.1619949114
发表时间：
2017-03-21
影响因子：
11.1
作者：
Leavitt, Matthew L.;Pieper, Florian;Martinez-Trujillo, Julio C.
通讯作者：
Martinez-Trujillo, Julio C.
影响因子：
3.4
作者：
Ploner, CJ;Gaymard, B;Pierrot-Deseilligny, C
通讯作者：
Pierrot-Deseilligny, C
影响因子：
1.9
作者：
Bressloff, Paul C.
通讯作者：
Bressloff, Paul C.
随机神经场中的脉冲分岔
影响因子：
2.1
作者：
Z. Kilpatrick;Grégory Faye
通讯作者：
Grégory Faye
一些神经场方程的平稳解的局部/全局分析
DOI：
10.1137/090773611
发表时间：
2009
期刊：
SIAM J. Appl. Dyn. Syst.
影响因子：
--
作者：
R. Veltz;O. Faugeras
通讯作者：
O. Faugeras