Neural noise can explain expansive, power-law nonlinearities in neural response functions
Neural noise can explain expansive, power-law nonlinearities in neural response functions
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DOI：
10.1152/jn.00425.2001
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发表时间：
2002-02-01
影响因子：
2.5
通讯作者：
Troyer, TW
中科院分区：
文献类型：
--
作者：
Miller, KD;Troyer, TW
许多初级视皮层简单细胞反应的现象学模型得出结论，细胞的放电率应该由其输入的大于1的幂次给出。这被称为扩展幂律非线性。然而，细胞内记录显示，不同的非线性，线性阈值函数，似乎给出了一个很好的预测从细胞的低通滤波电压响应的发射率。安德森等人使用基于线性阈值函数的模型，证明了电压噪声对于将具有对比度不变方向调谐的电压响应转换为具有对比度不变调谐的尖峰响应至关重要。我们提出了两个单独的结果澄清噪声平滑的线性阈值函数和幂律非线性之间的连接。首先，我们证明了解析幂律非线性是唯一的输入输出函数，将对比度不变的输入调谐到对比度不变的尖峰调谐。其次，我们研究了一个简单的模型，假设瞬时尖峰率是由电压和电压响应的线性阈值函数，包括显着的噪声模拟。我们表明，所得到的平均尖峰率是很好地描述了一个扩展的平均电压（平均多次试验）的幂律，平均电压保持小于约1.5 SD以上的阈值的噪声。最后，我们使用这个模型表明，安德森等人记录的噪声水平是一致的，在何种程度上的尖峰响应的方向调谐更急剧调整相对于电压响应的方向调谐。因此，神经元噪声可以鲁棒地产生幂律输入输出函数的形式经常假设为简单的细胞。
Many phenomenological models of the responses of simple cells in primary visual cortex have concluded that a cell's firing rate should be given by its input raised to a power greater than one. This is known as an expansive power-law nonlinearity. However, intracellular recordings have shown that a different nonlinearity, a linear-threshold function, appears to give a good prediction of firing rate from a cell's low-pass-filtered voltage response. Using a model based on a linear-threshold function, Anderson et al. showed that voltage noise was critical to converting voltage responses with contrast-invariant orientation tuning into spiking responses with contrast-invariant tuning. We present two separate results clarifying the connection between noise-smoothed linear-threshold functions and power-law nonlinearities. First, we prove analytically that a power-law nonlinearity is the only input-output function that converts contrast-invariant input tuning into contrast-invariant spike tuning. Second, we examine simulations of a simple model that assumes instantaneous spike rate is given by a linear-threshold function of voltage and voltage responses include significant noise. We show that the resulting average spike rate is well described by an expansive power law of the average voltage (averaged over multiple trials), provided that average voltage remains less than about 1.5 SDs of the noise above threshold. Finally, we use this model to show that the noise levels recorded by Anderson et al. are consistent with the degree to which the orientation tuning of spiking responses is more sharply tuned relative to the orientation tuning of voltage responses. Thus neuronal noise can robustly generate power-law input-output functions of the form frequently postulated for simple cells.