A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies.
A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies.
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DOI：
10.7554/elife.86552
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发表时间：
2023-12-12
期刊：
影响因子：
7.7
通讯作者：
Mazzucato L
中科院分区：
文献类型：
--
作者：
Stern M;Istrate N;Mazzucato L
许多物理和生物系统的时间活动，从复杂网络到神经回路，都表现出在很大的时间尺度上同时变化的波动。在同一皮层回路中同时记录的神经元中，观察到内在时间尺度的长尾分布。导致这种惊人的时间异质性的机制尚不清楚。在这里，我们表明，神经回路，赋予不同大小的异构神经组件，自然产生多个时间尺度的活动跨越几个数量级。我们开发了一个分析理论，使用率网络，支持与细胞类型特定的连接尖峰网络的模拟，解释神经时间尺度如何依赖于组装的大小，并表明我们的模型可以自然地解释长尾时间尺度分布观察到清醒的灵长类动物皮层。当通过依赖于时间的宽带输入驱动异质神经组件的递归网络时，我们发现大的和小的组件分别优先携带输入的慢谱分量和快谱分量。我们的研究结果表明，异质组件可以提供一个生物学上合理的机制，神经回路的时间输入信号的解混通过转换时间到空间的神经代码，通过频率选择性神经组件。
The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here, we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking networks with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in the awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies.
影响因子：
16.2
作者：
Kiani, Roozbeh;Cueva, Christopher J.;Reppas, John B.;Peixoto, Diogo;Ryu, Stephen I.;Newsome, William T.
通讯作者：
Newsome, William T.