Variance as a signature of neural computations during decision making.
Variance as a signature of neural computations during decision making.
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DOI：
10.1016/j.neuron.2010.12.037
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发表时间：
2011-02-24
期刊：
影响因子：
16.2
通讯作者：
Shadlen MN
中科院分区：
文献类型：
--
作者：
Churchland AK;Kiani R;Chaudhuri R;Wang XJ;Pouget A;Shadlen MN
传统上，对神经计算的理解是通过许多刺激重复或试验的平均放电率来提供的。我们追求的神经响应方差分析，揭示神经计算，不能从平均发射率的措施辨别。我们分析了单神经元记录从外侧顶内区（LIP），在知觉决策任务。使用双随机过程的总方差定律将尖峰计数方差分为两个分量：（i）将由具有给定速率的随机点过程产生的计数的方差，以及松散地（ii）将产生这些计数的速率的方差（即，“条件期望”）。条件期望的方差和相关性暴露了几种神经机制：决策前的放电率状态的混合物，决策形成过程中随机“证据”的积累，以及决策结束时的刻板反应。这些分析有助于区分几种不同的决策模型。
Traditionally, insights into neural computation have been furnished by averaged firing rates from many stimulus repetitions or trials. We pursue an analysis of neural response variance to unveil neural computations that cannot be discerned from measures of average firing rate. We analyzed single-neuron recordings from the lateral intraparietal area (LIP), during a perceptual decision-making task. Spike count variance was divided into two components using the law of total variance for doubly stochastic processes: (i) variance of counts that would be produced by a stochastic point process with a given rate, and loosely (ii) the variance of the rates that would produce those counts (i.e., “conditional expectation”). The variance and correlation of the conditional expectation exposed several neural mechanisms: mixtures of firing rate states preceding the decision, accumulation of stochastic “evidence” during decision formation, and a stereotyped response at decision end. These analyses help to differentiate among several alternative decision-making models.
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影响因子：
1.9
作者：
BRITTEN, KH;SHADLEN, MN;MOVSHON, JA
通讯作者：
MOVSHON, JA
影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
5.3
作者：
Cisek, Paul;Puskas, Genevieve Aude;El-Murr, Stephany
通讯作者：
El-Murr, Stephany
影响因子：
2.5
作者：
AERTSEN, AMHJ;GERSTEIN, GL;PALM, G
通讯作者：
PALM, G
影响因子：
1.9
作者：
Britten, KH;Newsome, WT;Movshon, JA
通讯作者：
Movshon, JA