Department of Mathematics, University of Utah Salt Lake City, UT, USA.
Front Comput Neurosci. 2012 Oct 29;6:90. doi: 10.3389/fncom.2012.00090. eCollection 2012.
We analyze the effects of extrinsic noise on traveling pulses in a neural field model of direction selectivity. The model consists of a one-dimensional scalar neural field with an asymmetric weight distribution consisting of an offset Mexican hat function. We first show how, in the absence of any noise, the system supports spontaneously propagating traveling pulses that can lock to externally moving stimuli. Using a separation of time-scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the wave from its uniformly translating position at long time-scales, and fluctuations in the wave profile around its instantaneous position at short time-scales. In the case of freely propagating pulses, the wandering is characterized by pure Brownian motion, whereas in the case of stimulus-locked pulses, it is given by an Ornstein-Uhlenbeck process. This establishes that stimulus-locked pulses are more robust to noise.
我们分析了外噪声对方向选择性神经场模型中传播脉冲的影响。该模型由一个具有不对称权重分布的一维标量神经场组成,权重分布由一个偏移的墨西哥帽函数组成。我们首先展示了在没有任何噪声的情况下,系统如何支持自发传播的传播脉冲,这些脉冲可以与外部移动刺激锁定。使用先前为随机反应扩散方程开发的时间尺度分离和微扰方法,我们展示了活动变量中的外噪声如何导致波在长时间尺度上从其均匀平移位置的扩散样位移(徘徊),以及波轮廓在短时间尺度上围绕其瞬时位置的波动。在自由传播脉冲的情况下,徘徊由纯布朗运动表征,而在刺激锁定脉冲的情况下,由奥恩斯坦-乌伦贝克过程表征。这表明刺激锁定的脉冲对噪声更具鲁棒性。