Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA.
Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA.
Cereb Cortex. 2019 Mar 1;29(3):937-951. doi: 10.1093/cercor/bhy001.
The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.
皮质的突触连接具有可塑性，经验会塑造神经元之间持续的相互作用。关于尖峰时间依赖性可塑性（STDP）的理论研究主要集中在神经元对或大规模模拟上。目前缺乏一种简单的分析方法来描述尖峰时间相关性如何快速影响微观和宏观网络结构。我们为递归网络中的 STDP 开发了一个低维平均场理论，并展示了具有共享刺激偏好的强耦合神经元集合的出现。在训练后，这种连接在自发动力学过程中会被尖峰时间相关性主动加强。此外，细胞集合的刺激编码会被这些内部产生的尖峰相关性主动维持，这表明噪声相关性在神经编码中具有新的作用。集合形成通常与基于发放率的可塑性方案有关；我们的理论提供了一个替代和互补的框架，其中精细的时间相关性和 STDP 形成并主动维持皮质网络中的学习结构。
