Anomalous Neuronal Responses to Fluctuated Inputs
Anomalous Neuronal Responses to Fluctuated Inputs
复制标题
对波动输入的异常神经元反应
DOI：
10.1103/physreve.92.042705
复制
发表时间：
2015
影响因子：
2.4
通讯作者：
Ryosuke Hosaka and Yutaka Sakai
中科院分区：
文献类型：
--
作者：
Hosaka R;Nakajima T;Aihara K;Yamaguchi Y;Mushiake H.;石原礼子 馬場園明;石原礼子 馬場園明;Ryosuke Hosaka and Yutaka Sakai
皮层神经元的不规则放电被认为是由兴奋性和抑制性突触输入的平衡产生的高度波动的驱动造成的。先前的一项研究报告了Hodgkin-Huxley神经元对波动输入的异常反应，其中棘波序列的不规则与输入的不规则成反比。在目前的研究中，我们用Hindmarsh-Rose神经元模型、基于MAP的模型和简单的棘波间隔分布的简单混合来研究这些异常反应的起源。首先，我们指定了Hindmarsh-Rose模型中分叉的参数区域，并证实该模型再现了鞍结点和亚临界Hopf分叉动力学中的反常响应。对于这两种分叉，Hindmarsh-Rose模型在静止态和重复激发态都表现出双稳态，这表明双稳态是异常投入产出关系的根源。同样，包含双稳的MAP模型复制了异常反应，而不包含双稳的模型则没有。这些结果得到了其他发现的支持，即异常反应是通过模拟具有两种不同尖峰间隔分布的混合的双稳态放电来再现的。棘波序列的去相关性是神经信息处理的重要内容。对于这样的尖峰列车去相关，不规则发射是关键。我们的结果表明，在涉及双稳态的条件下，波动驱动，甚至是弱驱动，可能会出现不规则的激发。因此，异常反应有助于大脑的有效处理。
The irregular firing of a cortical neuron is thought to result from a highly fluctuating drive that is generated by the balance of excitatory and inhibitory synaptic inputs. A previous study reported anomalous responses of the Hodgkin-Huxley neuron to the fluctuated inputs where an irregularity of spike trains is inversely proportional to an input irregularity. In the current study, we investigated the origin of these anomalous responses with the Hindmarsh-Rose neuron model, map-based models, and a simple mixture of interspike interval distributions. First, we specified the parameter regions for the bifurcations in the Hindmarsh-Rose model, and we confirmed that the model reproduced the anomalous responses in the dynamics of the saddle-node and subcritical Hopf bifurcations. For both bifurcations, the Hindmarsh-Rose model shows bistability in the resting state and the repetitive firing state, which indicated that the bistability was the origin of the anomalous input-output relationship. Similarly, the map-based model that contained bistability reproduced the anomalous responses, while the model without bistability did not. These results were supported by additional findings that the anomalous responses were reproduced by mimicking the bistable firing with a mixture of two different interspike interval distributions. Decorrelation of spike trains is important for neural information processing. For such spike train decorrelation, irregular firing is key. Our results indicated that irregular firing can emerge from fluctuating drives, even weak ones, under conditions involving bistability. The anomalous responses, therefore, contribute to efficient processing in the brain.