Durrant Simon, Kang Yanmei, Stocks Nigel, Feng Jianfeng
Department of Informatics, Sussex University, Brighton BN1 9QH, United Kingdom.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84(1 Pt 1):011923. doi: 10.1103/PhysRevE.84.011923. Epub 2011 Jul 25.
Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.
在积分发放神经元的背景下研究了超阈值随机共振（SSR），重点关注相关性在神经元放电中的作用。我们采用了一个基于脉冲神经元网络的模型，该网络接收由阶梯输入信号刺激的泊松过程建模的突触输入。平滑后的总体放电率提供了一个输出信号，并针对具有不同噪声水平和不同神经元数量的网络计算了该信号与输入之间的互信息。发现在这种情况下存在SSR效应。然后，我们研究了一种更符合生物物理原理的情况，即噪声不是直接控制的，而是通过输入之间的相关性进行调节。在这种情况下，非零噪声下的SSR效应仍然存在，且能改善信息传输，并且发现输入之间的负相关性是最优的。最后，在该模型的背景下对SSR的研究揭示了它与更传统的随机共振的联系，并显示了超阈值和亚阈值成分之间的权衡。我们结合有关神经元放电相关性的现有经验证据来讨论这些结果。
