Predicting single-neuron activity in locally connected networks.
Predicting single-neuron activity in locally connected networks.
复制标题
DOI：
10.1162/neco_a_00343
复制
发表时间：
2012-10
影响因子：
2.9
通讯作者：
Anderson WS
中科院分区：
文献类型：
--
作者：
Azhar F;Anderson WS
鉴于先进的实验技术允许同时记录多个单元，神经元群体中协调活动的表征重新引起了人们的兴趣。在体外和体内制备中，当自发活动和受到外部刺激时，附近的神经元表现出协调的反应。最近的研究将这些协调反应与行为联系起来，表明感觉运动皮层手臂相关区域的小神经元群可以可靠地预测行为猴子和人类的单个神经元峰值。我们使用一个类似的点过程模型来研究这一现象，结果表明，在大脑皮层响应随机背景输入的计算模型中，人们同样能够通过考虑单个神经元自身的峰值历史，以及随机采样的附近神经元集合的峰值历史，来预测单个神经元的未来状态。该模型显示了真实的皮层结构，并在研究的两种不同的连接方案中显示了爆发事件。我们推测，我们在这些实例中发现的基线可预测性是更广泛考虑的局部连接网络的特征。
The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques that allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active and when subject to external stimuli. Recent work has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm-related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon using an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered.