Delaney Thomas J, O'Donnell Cian
School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol, UK.
School of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry, UK.
Netw Neurosci. 2023 Jun 30;7(2):731-742. doi: 10.1162/netn_a_00309. eCollection 2023.
Ensembles of neurons are thought to be coactive when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast subsecond timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel.
当参与大脑计算时,神经元集群被认为是共同激活的。然而,尚不清楚是什么原则决定一个集群是局限于单个脑区,还是跨越多个脑区。为了解决这个问题,我们分析了来自清醒小鼠九个脑区同时记录的数百个神经元的电生理神经群体数据。在快速的亚秒时间尺度上,同一脑区内神经元对之间的脉冲计数相关性强于分布在不同脑区的神经元对之间的相关性。相比之下,在较慢的时间尺度上,区域内和区域间的脉冲计数相关性相似。高放电率神经元对之间的相关性比低放电率神经元对之间的相关性对时间尺度的依赖性更强。我们将一个集群检测算法应用于神经相关性数据,发现快速时间尺度下每个集群大多包含在单个脑区内,而在较慢时间尺度下集群跨越多个脑区。这些结果表明,小鼠大脑可能并行执行快速局部和慢速全局计算。