Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents.
Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents.
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DOI：
10.3389/fncir.2013.00190
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发表时间：
2013
影响因子：
3.5
通讯作者：
Schwarz C
中科院分区：
文献类型：
--
作者：
Chagas AM;Theis L;Sengupta B;Stüttgen MC;Bethge M;Schwarz C
感觉受体决定可用于感知的信息的类型和数量。在这里，我们用神经系统识别的方法对大鼠须系统中主要事件传递的信息进行了量化和表征。利用经典的信息理论工具直接法（DM）量化主传入传递了“多少”信息，揭示了主传入传递了大量的信息（高达529比特/秒）。通过对瞬时脉冲触发的运动刺激特征的信息论分析，对初级传入信号编码的内容进行了功能分析。在被测试的运动学变量中——位置、速度和加速度——主要的传入尖峰编码速度最好。其他两个变量有助于信息传递，但只有在与速度结合的情况下。我们进一步揭示了在主要传入信息传递中起作用的另外三个特征。首先，初级传入尖峰表现出对分离良好的多重刺激的偏好（即，三个瞬时运动变量的组合分离良好）。其次，神经元对刺激轨迹的短条（峰值前10毫秒）敏感；第三，它们表现出峰值模式（精确的双峰和三重峰）。为了处理这些复杂性，我们使用了一种灵活的概率神经元模型，将高斯分布拟合到峰值触发的刺激分布中，该模型定量地捕获了上述特征的贡献，并允许我们实现DM所指示的总信息率的完整功能分析。我们发现瞬时位置，速度和加速度解释了总信息率的50%左右。增加10 ms的预峰间隔刺激轨迹达到80-90%。最后的10-20%被发现是由于脉冲爆发的非线性编码。
Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of “how much” information is conveyed by primary afferents, using the direct method (DM), a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on “what” is coded by primary afferents. Amongst the kinematic variables tested—position, velocity, and acceleration—primary afferent spikes encoded velocity best. The other two variables contributed to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e., well separated sets of combinations of the three instantaneous kinematic variables). Secondly, neurons are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the DM. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80–90%. The final 10–20% were found to be due to non-linear coding by spike bursts.
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