Modeling cross-modal enhancement and modality-specific suppression in multisensory neurons
Modeling cross-modal enhancement and modality-specific suppression in multisensory neurons
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DOI：
10.1162/08997660360581903
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发表时间：
2003-04-01
影响因子：
2.9
通讯作者：
Anastasio, TJ
中科院分区：
文献类型：
--
作者：
Patton, PE;Anastasio, TJ
当神经对一个通道的刺激的反应被另一个不同通道的刺激增强时，就会发生跨通道增强(CME)。同一通道的成对刺激永远不会产生超加性增强，但可能会产生通道特异性抑制(MSS)，即对一种通道的刺激的反应被同一通道的另一种刺激减弱。CME和MSS都被描述为上丘(DSC)深层神经元，但其神经机制仍不清楚。以前的研究人员认为，CME涉及一个乘性放大器，可能由N-甲基D-天冬氨酸(NMDA)受体介导，该受体通过跨通道而不是通道特异性的输入参与。我们先前假设DSC神经元使用多感觉输入来使用贝叶斯规则来计算目标的后验概率。贝叶斯规则模型再现了CME的主要特征。在这里，我们使用我们的模型的简单神经实现来模拟CME和MSS，并论证乘法过程对于CME来说不是必需的，但可能需要用来表示输入方差和协方差。产生CME只需要输入和生物神经元简单模型的阈值和饱和特性的加权和。当自发输入和驱动输入具有不同的方差和协方差时，乘法节点允许精确计算后验目标概率。对于药物阻断NMDA受体对DSC神经元多感觉反应的影响，贝叶斯规则模型的神经实现比乘性放大假说更好地解释了这一假设。神经实现也解释了MSS，只给出了附加的假设，即相同通道的输入通道比不同通道的通道具有更多的自发协方差。
Cross-modal enhancement (CME) occurs when the neural response to a stimulus of one modality is augmented by another stimulus of a different modality. Paired stimuli of the same modality never produce supra-additive enhancement but may produce modality-specific suppression (MSS), in which the response to a stimulus of one modality is diminished by another stimulus of the same modality. Both CME and MSS have been described for neurons in the deep layers of the superior colliculus (DSC), but their neural mechanisms remain unknown. Previous investigators have suggested that CME involves a multiplicative amplifier, perhaps mediated by N-methyl D-aspartate (NMDA) receptors, which is engaged by cross-modal but not modality-specific input. We previously postulated that DSC neurons use multisensory input to compute the posterior probability of a target using Bayes' rule. The Bayes' rule model reproduces the major features of CME. Here we use simple neural implementations of our model to simulate both CME and MSS and to argue that multiplicative processes are not needed for CME, but may be needed to represent input variance and covariance. Producing CME requires only weighted summation of inputs and the threshold and saturation properties of simple models of biological neurons. Multiplicative nodes allow accurate computation of posterior target probabilities when the spontaneous and driven inputs have unequal variances and covariances. Neural implementations of the Bayes' rule model account better than the multiplicative amplifier hypothesis for the effects of pharmacological blockade of NMDA receptors on the multisensory responses of DSC neurons. The neural implementations also account for MSS, given only the added hypothesis that input channels of the same modality have more spontaneous covariance than those of different modalities.