1 Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland.
Int J Neural Syst. 2017 Dec;27(8):1750044. doi: 10.1142/S0129065717500447. Epub 2017 Sep 7.
Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.
在许多物种的大脑区域中都观察到了精确时间的神经元活动序列。同步放电链是一个成熟的模型，可以解释这种序列。然而，目前尚不清楚在何种条件下，同步放电链可以通过自组织在最初无结构的网络中发展。这项工作表明，通过由全局神经元活动调制的尖峰时间依赖可塑性（STDP），稀疏随机网络中会出现长的同步放电链。学习规则促使神经元多次参与链或多个链。这种神经元的重复使用已经在实验中观察到，对于高容量是必要的。稀疏网络防止链变得短而循环，并表明形成特定突触对于链的形成不是必需的。在一个简单的二进制阈值神经元网络中对学习规则进行分析，揭示了新出现的链的渐近最优长度。理论结果推广到基于电导的放电整合（LIF）神经元的模拟网络。作为出现的链的一个应用，我们提出了用于精确时间神经元活动序列的一次性记忆。
