Partitioning neuronal variability.
Partitioning neuronal variability.
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DOI：
10.1038/nn.3711
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发表时间：
2014-06
影响因子：
25
通讯作者：
Simoncelli, Eero P.
中科院分区：
文献类型：
--
作者：
Goris, Robbe L. T.;Movshon, J. Anthony;Simoncelli, Eero P.
感觉神经元的反应在重复测量中有所不同。这种变异通常被视为神经元或神经回路内产生的随机性。但是，变异性的一部分是由于不纯粹的感觉的因素（例如唤醒，注意力和适应性）引起的兴奋性的波动。为了隔离这些波动，我们开发了一个模型，在该模型中，尖峰是由泊松过程产生的，该过程的速率是驱动器的驱动器的乘积，而增益汇总了刺激无关的调节性影响对兴奋性的影响。该模型提供了猕猴LGN，V1，V2和MT中视觉神经元反应分布的准确说明，揭示了可变性很大程度上源于兴奋性波动，这些兴奋性波动随着时间的推移和神经元之间的相关性，并且沿着视觉量的强度增加了。路径。该模型为观察到的响应变异性和对发射率的协方差的系统依赖性提供了简约的解释。
Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention, and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin, and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque LGN, V1, V2, and MT, revealing that variability originates in large part from excitability fluctuations which are correlated over time and between neurons, and which increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.
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影响因子：
5.4
作者：
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通讯作者：
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影响因子：
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通讯作者：
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影响因子：
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