Hosaka Ryosuke, Sakai Yutaka
Department of Applied Mathematics, Fukuoka University, Fukuoka Prefecture 814-0180, Japan.
Tamagawa University Brain Science Institute, Tokyo 194-8610, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Oct;92(4):042705. doi: 10.1103/PhysRevE.92.042705. Epub 2015 Oct 7.
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.
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平衡所产生的高度波动驱动导致的。先前的一项研究报告了霍奇金-赫胥黎神经元对波动输入的异常反应,其中脉冲序列的不规则性与输入不规则性成反比。在当前的研究中,我们使用辛德马什-罗斯神经元模型、基于映射的模型以及简单的峰间期分布混合模型来研究这些异常反应的起源。首先,我们确定了辛德马什-罗斯模型中分支的参数区域,并证实该模型在鞍结和亚临界霍普夫分支的动力学中再现了异常反应。对于这两种分支,辛德马什-罗斯模型在静息状态和重复放电状态下都表现出双稳性,这表明双稳性是异常输入-输出关系的起源。同样,包含双稳性的基于映射的模型再现了异常反应,而没有双稳性的模型则没有。通过用两种不同峰间期分布的混合来模拟双稳放电再现了异常反应这一额外发现支持了这些结果。脉冲序列的去相关对于神经信息处理很重要。对于这种脉冲序列去相关,不规则放电是关键。我们的结果表明,在涉及双稳性的条件下,即使是微弱的波动驱动也能产生不规则放电。因此,这些异常反应有助于大脑中的高效处理。